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For: Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278:563-577. [PMID: 26579733 DOI: 10.1148/radiol.2015151169] [Cited by in Crossref: 2333] [Cited by in F6Publishing: 2100] [Article Influence: 333.3] [Reference Citation Analysis]
Number Citing Articles
1 Coroller TP, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee SW, Mak RH, Aerts HJ. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 2016;119:480-6. [PMID: 27085484 DOI: 10.1016/j.radonc.2016.04.004] [Cited by in Crossref: 155] [Cited by in F6Publishing: 152] [Article Influence: 25.8] [Reference Citation Analysis]
2 Homayounieh F, Saini S, Mostafavi L, Doda Khera R, Sühling M, Schmidt B, Singh R, Flohr T, Kalra MK. Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT. Int J CARS 2020;15:1727-36. [DOI: 10.1007/s11548-020-02212-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Golia Pernicka JS, Gagniere J, Chakraborty J, Yamashita R, Nardo L, Creasy JM, Petkovska I, Do RRK, Bates DDB, Paroder V, Gonen M, Weiser MR, Simpson AL, Gollub MJ. Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation. Abdom Radiol (NY) 2019;44:3755-63. [PMID: 31250180 DOI: 10.1007/s00261-019-02117-w] [Cited by in Crossref: 23] [Cited by in F6Publishing: 22] [Article Influence: 11.5] [Reference Citation Analysis]
4 De Santi B, Spaggiari G, Granata AR, Romeo M, Molinari F, Simoni M, Santi D. From subjective to objective: A pilot study on testicular radiomics analysis as a measure of gonadal function. Andrology 2021. [PMID: 34817934 DOI: 10.1111/andr.13131] [Reference Citation Analysis]
5 Yang T, Zhou Y, Li L, Zhu C. DCU-Net: Multi-scale U-Net for brain tumor segmentation. J Xray Sci Technol 2020;28:709-26. [PMID: 32444591 DOI: 10.3233/XST-200650] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
6 Pérez-Beteta J, Molina-García D, Martínez-González A, Henares-Molina A, Amo-Salas M, Luque B, Arregui E, Calvo M, Borrás JM, Martino J, Velásquez C, Meléndez-Asensio B, de Lope ÁR, Moreno R, Barcia JA, Asenjo B, Benavides M, Herruzo I, Lara PC, Cabrera R, Albillo D, Navarro M, Pérez-Romasanta LA, Revert A, Arana E, Pérez-García VM. Morphological MRI-based features provide pretreatment survival prediction in glioblastoma. Eur Radiol 2019;29:1968-77. [PMID: 30324390 DOI: 10.1007/s00330-018-5758-7] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.8] [Reference Citation Analysis]
7 Varghese B, Chen F, Hwang D, Palmer SL, De Castro Abreu AL, Ukimura O, Aron M, Aron M, Gill I, Duddalwar V, Pandey G. Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Sci Rep 2019;9:1570. [PMID: 30733585 DOI: 10.1038/s41598-018-38381-x] [Cited by in Crossref: 27] [Cited by in F6Publishing: 26] [Article Influence: 9.0] [Reference Citation Analysis]
8 Li M, Li X, Guo Y, Miao Z, Liu X, Guo S, Zhang H. Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases. Quant Imaging Med Surg 2020;10:397-414. [PMID: 32190566 DOI: 10.21037/qims.2019.12.16] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Tian H, Wu H, Wu G, Xu G. Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI. Biomed Res Int 2020;2020:3872314. [PMID: 32509858 DOI: 10.1155/2020/3872314] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
10 Kwon MR, Shin JH, Park H, Cho H, Kim E, Hahn SY. Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma. J Clin Med 2020;9:E2156. [PMID: 32650493 DOI: 10.3390/jcm9072156] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
11 Tobaly D, Santinha J, Sartoris R, Dioguardi Burgio M, Matos C, Cros J, Couvelard A, Rebours V, Sauvanet A, Ronot M, Papanikolaou N, Vilgrain V. CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas. Cancers (Basel) 2020;12:E3089. [PMID: 33114028 DOI: 10.3390/cancers12113089] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
12 Dieckmeyer M, Inhuber S, Schläger S, Weidlich D, Mookiah MRK, Subburaj K, Burian E, Sollmann N, Kirschke JS, Karampinos DC, Baum T. Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water-Fat MRI. Diagnostics (Basel) 2021;11:302. [PMID: 33668624 DOI: 10.3390/diagnostics11020302] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Yao X, Sun C, Xiong F, Zhang X, Cheng J, Wang C, Ye Y, Hong N, Wang L, Liu Z, Meng X, Wang Y, Tian J. Radiomic signature-based nomogram to predict disease-free survival in stage II and III colon cancer. Eur J Radiol 2020;131:109205. [PMID: 32871292 DOI: 10.1016/j.ejrad.2020.109205] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
14 Tan Y, Mu W, Wang XC, Yang GQ, Gillies RJ, Zhang H. Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: A preliminary study. Eur J Radiol 2020;124:108785. [PMID: 32004731 DOI: 10.1016/j.ejrad.2019.108785] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Gallaher JA, Massey SC, Hawkins-Daarud A, Noticewala SS, Rockne RC, Johnston SK, Gonzalez-Cuyar L, Juliano J, Gil O, Swanson KR, Canoll P, Anderson ARA. From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response. PLoS Comput Biol 2020;16:e1007672. [PMID: 32101537 DOI: 10.1371/journal.pcbi.1007672] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
16 Kim M, Jung SC, Park JE, Park SY, Lee H, Choi KM. Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factors. Eur Radiol 2021;31:6457-70. [PMID: 33733690 DOI: 10.1007/s00330-021-07760-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Jiang X, Zou X, Sun J, Zheng A, Su C. A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer. Contrast Media Mol Imaging 2020;2020:5418364. [PMID: 32922222 DOI: 10.1155/2020/5418364] [Reference Citation Analysis]
18 Attenberger UI, Langs G. How does Radiomics actually work? - Review. Rofo 2021;193:652-7. [PMID: 33264805 DOI: 10.1055/a-1293-8953] [Reference Citation Analysis]
19 Limkin EJ, Sun R. Radiomics to predict response to immunotherapy: an imminent reality? Future Oncol 2020;16:1673-6. [PMID: 32447997 DOI: 10.2217/fon-2020-0015] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 Bobholz SA, Lowman AK, Barrington A, Brehler M, McGarry S, Cochran EJ, Connelly J, Mueller WM, Agarwal M, O'Neill D, Nencka AS, Banerjee A, LaViolette PS. Radiomic Features of Multiparametric MRI Present Stable Associations With Analogous Histological Features in Patients With Brain Cancer. Tomography 2020;6:160-9. [PMID: 32548292 DOI: 10.18383/j.tom.2019.00029] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
21 Denecke K, Gabarron E, Grainger R, Konstantinidis ST, Lau A, Rivera-Romero O, Miron-Shatz T, Merolli M. Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications. Yearb Med Inform 2019;28:165-73. [PMID: 31022749 DOI: 10.1055/s-0039-1677902] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
22 Chianca V, Cuocolo R, Gitto S, Albano D, Merli I, Badalyan J, Cortese MC, Messina C, Luzzati A, Parafioriti A, Galbusera F, Brunetti A, Sconfienza LM. Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study. Eur J Radiol 2021;137:109586. [PMID: 33610852 DOI: 10.1016/j.ejrad.2021.109586] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
23 Mattonen SA, Ward AD, Palma DA. Pulmonary imaging after stereotactic radiotherapy-does RECIST still apply? Br J Radiol 2016;89:20160113. [PMID: 27245137 DOI: 10.1259/bjr.20160113] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 2.3] [Reference Citation Analysis]
24 Psutka SP, Singer EA, Gore J. A 25-year perspective on advances in the study of the epidemiology, disparities, and outcomes of urologic cancers. Urol Oncol 2021;39:595-601. [PMID: 33934967 DOI: 10.1016/j.urolonc.2021.03.019] [Reference Citation Analysis]
25 Wang L, Xu N, Song J. Decoding intra-tumoral spatial heterogeneity on radiological images using the Hilbert curve. Insights Imaging 2021;12:154. [PMID: 34716809 DOI: 10.1186/s13244-021-01100-8] [Reference Citation Analysis]
26 Rundo L, Ledda RE, di Noia C, Sala E, Mauri G, Milanese G, Sverzellati N, Apolone G, Gilardi MC, Messa MC, Castiglioni I, Pastorino U. A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules. Diagnostics (Basel) 2021;11:1610. [PMID: 34573951 DOI: 10.3390/diagnostics11091610] [Reference Citation Analysis]
27 Ma X, Shen F, Jia Y, Xia Y, Li Q, Lu J. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging. 2019;19:86. [PMID: 31747902 DOI: 10.1186/s12880-019-0392-7] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 6.7] [Reference Citation Analysis]
28 Li R. Peritumoral Radiomics and Predicting Treatment Response. JAMA Netw Open 2020;3:e2016125. [PMID: 32910193 DOI: 10.1001/jamanetworkopen.2020.16125] [Reference Citation Analysis]
29 Gainey M, Carles M, Mix M, Meyer PT, Bock M, Grosu AL, Baltas D. Biological imaging for individualized therapy in radiation oncology: part I physical and technical aspects. Future Oncol 2018;14:737-49. [PMID: 29521520 DOI: 10.2217/fon-2017-0464] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
30 Yi X, Xiao Q, Zeng F, Yin H, Li Z, Qian C, Wang C, Lei G, Xu Q, Li C, Li M, Gong G, Zee C, Guan X, Liu L, Chen BT. Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma. Front Oncol 2020;10:570396. [PMID: 33585193 DOI: 10.3389/fonc.2020.570396] [Reference Citation Analysis]
31 Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F. Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer. Sci Rep 2017;7:46349. [PMID: 28418006 DOI: 10.1038/srep46349] [Cited by in Crossref: 107] [Cited by in F6Publishing: 100] [Article Influence: 21.4] [Reference Citation Analysis]
32 Dinis Fernandes C, Dinh CV, Walraven I, Heijmink SW, Smolic M, van Griethuysen JJM, Simões R, Losnegård A, van der Poel HG, Pos FJ, van der Heide UA. Biochemical recurrence prediction after radiotherapy for prostate cancer with T2w magnetic resonance imaging radiomic features. Phys Imaging Radiat Oncol 2018;7:9-15. [PMID: 33458399 DOI: 10.1016/j.phro.2018.06.005] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 2.8] [Reference Citation Analysis]
33 Yankeelov TE, An G, Saut O, Luebeck EG, Popel AS, Ribba B, Vicini P, Zhou X, Weis JA, Ye K, Genin GM. Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success. Ann Biomed Eng 2016;44:2626-41. [PMID: 27384942 DOI: 10.1007/s10439-016-1691-6] [Cited by in Crossref: 46] [Cited by in F6Publishing: 34] [Article Influence: 7.7] [Reference Citation Analysis]
34 Di Re AM, Sun Y, Sundaresan P, Hau E, Toh JWT, Gee H, Or M, Haworth A. MRI radiomics in the prediction of therapeutic response to neoadjuvant therapy for locoregionally advanced rectal cancer: a systematic review. Expert Rev Anticancer Ther 2021;21:425-49. [PMID: 33289435 DOI: 10.1080/14737140.2021.1860762] [Reference Citation Analysis]
35 Shao X, Niu R, Shao X, Jiang Z, Wang Y. Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules. EJNMMI Res 2020;10:80. [PMID: 32661639 DOI: 10.1186/s13550-020-00668-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
36 Yang J, Wu Q, Xu L, Wang Z, Su K, Liu R, Yen EA, Liu S, Qin J, Rong Y, Lu Y, Niu T. Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer. Radiother Oncol 2020;150:89-96. [PMID: 32531334 DOI: 10.1016/j.radonc.2020.06.004] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
37 Choi YS, Ahn SS, Chang JH, Kang SG, Kim EH, Kim SH, Jain R, Lee SK. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction. Eur Radiol 2020;30:3834-42. [PMID: 32162004 DOI: 10.1007/s00330-020-06737-5] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
38 Sun H, Qu H, Chen L, Wang W, Liao Y, Zou L, Zhou Z, Wang X, Zhou S. Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning. Eur Radiol 2019;29:6152-62. [PMID: 31444599 DOI: 10.1007/s00330-019-06372-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 3.3] [Reference Citation Analysis]
39 Liu Y, Shi H, Huang S, Chen X, Zhou H, Chang H, Xia Y, Wang G, Yang X. Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images. Quant Imaging Med Surg 2019;9:1288-302. [PMID: 31448214 DOI: 10.21037/qims.2019.07.08] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 4.3] [Reference Citation Analysis]
40 Tang L, Wang XJ, Baba H, Giganti F. Gastric cancer and image-derived quantitative parameters: Part 2-a critical review of DCE-MRI and 18F-FDG PET/CT findings. Eur Radiol 2020;30:247-60. [PMID: 31392480 DOI: 10.1007/s00330-019-06370-x] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
41 Meng Y, Zhang H, Li Q, Xing P, Liu F, Cao K, Fang X, Li J, Yu J, Feng X, Ma C, Wang L, Jiang H, Lu J, Bian Y, Shao C. Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in Patients With Pancreatic Ductal Adenocarcinoma. J Magn Reson Imaging 2021. [PMID: 33890347 DOI: 10.1002/jmri.27648] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
42 Zhang Y, Shu Z, Ye Q, Chen J, Zhong J, Jiang H, Wu C, Yu T, Pang P, Ma T, Lin C. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics. Front Oncol 2021;11:633596. [PMID: 33747956 DOI: 10.3389/fonc.2021.633596] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
43 Pfaehler E, Zwanenburg A, de Jong JR, Boellaard R. RaCaT: An open source and easy to use radiomics calculator tool. PLoS One 2019;14:e0212223. [PMID: 30785937 DOI: 10.1371/journal.pone.0212223] [Cited by in Crossref: 18] [Cited by in F6Publishing: 22] [Article Influence: 6.0] [Reference Citation Analysis]
44 Crisi G, Filice S. Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach. J Neuroimaging 2020;30:458-62. [PMID: 32374045 DOI: 10.1111/jon.12724] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
45 Bae S, Choi YS, Sohn B, Ahn SS, Lee SK, Yang J, Kim J. Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach. Yonsei Med J 2020;61:895-900. [PMID: 32975065 DOI: 10.3349/ymj.2020.61.10.895] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
46 Kuthuru S, Deaderick W, Bai H, Su C, Vu T, Monga V, Rao A. A Visually Interpretable, Dictionary-Based Approach to Imaging-Genomic Modeling, With Low-Grade Glioma as a Case Study. Cancer Inform 2018;17:1176935118802796. [PMID: 30305794 DOI: 10.1177/1176935118802796] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 1.8] [Reference Citation Analysis]
47 Lee SH, Park H, Ko ES. Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review. Korean J Radiol 2020;21:779-92. [PMID: 32524780 DOI: 10.3348/kjr.2019.0855] [Cited by in Crossref: 16] [Cited by in F6Publishing: 12] [Article Influence: 8.0] [Reference Citation Analysis]
48 Lewis S, Peti S, Hectors SJ, King M, Rosen A, Kamath A, Putra J, Thung S, Taouli B. Volumetric quantitative histogram analysis using diffusion-weighted magnetic resonance imaging to differentiate HCC from other primary liver cancers. Abdom Radiol (NY) 2019;44:912-22. [PMID: 30712136 DOI: 10.1007/s00261-019-01906-7] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
49 Ytre-Hauge S, Dybvik JA, Lundervold A, Salvesen ØO, Krakstad C, Fasmer KE, Werner HM, Ganeshan B, Høivik E, Bjørge L, Trovik J, Haldorsen IS. Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J Magn Reson Imaging 2018;48:1637-47. [PMID: 30102441 DOI: 10.1002/jmri.26184] [Cited by in Crossref: 45] [Cited by in F6Publishing: 41] [Article Influence: 11.3] [Reference Citation Analysis]
50 Näslund O, Smits A, Förander P, Laesser M, Bartek J Jr, Gempt J, Liljegren A, Daxberg EL, Jakola AS. Amino acid tracers in PET imaging of diffuse low-grade gliomas: a systematic review of preoperative applications. Acta Neurochir (Wien) 2018;160:1451-60. [PMID: 29797098 DOI: 10.1007/s00701-018-3563-3] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 2.8] [Reference Citation Analysis]
51 Zhu T, Das S, Wong TZ. Integration of PET/MR Hybrid Imaging into Radiation Therapy Treatment. Magn Reson Imaging Clin N Am 2017;25:377-430. [PMID: 28390536 DOI: 10.1016/j.mric.2017.01.001] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
52 Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 2020;116:2040-54. [PMID: 32090243 DOI: 10.1093/cvr/cvaa021] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 18.0] [Reference Citation Analysis]
53 Zhang D, Wei Q, Wu GG, Zhang XY, Lu WW, Lv WZ, Liao JT, Cui XW, Ni XJ, Dietrich CF. Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound. Front Oncol 2021;11:709339. [PMID: 34557410 DOI: 10.3389/fonc.2021.709339] [Reference Citation Analysis]
54 Yang B, Guo L, Lu G, Shan W, Duan L, Duan S. Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma. Cancer Manag Res 2019;11:7825-34. [PMID: 31695487 DOI: 10.2147/CMAR.S217887] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
55 Lao J, Chen Y, Li ZC, Li Q, Zhang J, Liu J, Zhai G. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. Sci Rep. 2017;7:10353. [PMID: 28871110 DOI: 10.1038/s41598-017-10649-8] [Cited by in Crossref: 209] [Cited by in F6Publishing: 161] [Article Influence: 41.8] [Reference Citation Analysis]
56 Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging. 2020;52:998-1018. [PMID: 31276247 DOI: 10.1002/jmri.26852] [Cited by in Crossref: 25] [Cited by in F6Publishing: 21] [Article Influence: 8.3] [Reference Citation Analysis]
57 Li Y, Han G, Wu X, Li Z, Zhao K, Zhang Z, Liu Z, Liang C. Normalization of multicenter CT radiomics by a generative adversarial network method. Phys Med Biol 2020. [PMID: 32209747 DOI: 10.1088/1361-6560/ab8319] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
58 Qian Z, Li Y, Sun Z, Fan X, Xu K, Wang K, Li S, Zhang Z, Jiang T, Liu X, Wang Y. Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction. Aging (Albany NY) 2018;10:2884-99. [PMID: 30362964 DOI: 10.18632/aging.101594] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
59 Wu W, Li J, Ye J, Wang Q, Zhang W, Xu S. Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning. Front Oncol 2021;11:639062. [PMID: 33791225 DOI: 10.3389/fonc.2021.639062] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
60 Razik A, Das CJ, Sharma R, Malla S, Sharma S, Seth A, Srivastava DN. Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study. Br J Radiol 2021;94:20201114. [PMID: 33882245 DOI: 10.1259/bjr.20201114] [Reference Citation Analysis]
61 Wei L, Cui C, Xu J, Kaza R, El Naqa I, Dewaraja YK. Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics. EJNMMI Phys 2020;7:74. [PMID: 33296050 DOI: 10.1186/s40658-020-00340-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
62 Whitney HM, Li H, Ji Y, Liu P, Giger ML. Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J Med Imaging (Bellingham) 2020;7:012707. [PMID: 32206682 DOI: 10.1117/1.JMI.7.1.012707] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
63 Liu Q, Li J, Liu F, Yang W, Ding J, Chen W, Wei Y, Li B, Zheng L. A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy. Cancer Imaging 2020;20:82. [PMID: 33198809 DOI: 10.1186/s40644-020-00360-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
64 Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, Haworth A. Multiparametric MRI and radiomics in prostate cancer: a review. Australas Phys Eng Sci Med 2019;42:3-25. [PMID: 30762223 DOI: 10.1007/s13246-019-00730-z] [Cited by in Crossref: 28] [Cited by in F6Publishing: 23] [Article Influence: 9.3] [Reference Citation Analysis]
65 Yang H, Son NH, Lee SH, Kim D, Kim HJ, Cha IH, Nam W. Predictive modelling of level IIb lymph node metastasis in oral squamous cell carcinoma. Sci Rep 2021;11:17562. [PMID: 34475441 DOI: 10.1038/s41598-021-96827-1] [Reference Citation Analysis]
66 Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, Mao R, Li F, Xiao Y, Wang Y, Hu Y, Yu J, Zhou J. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 2020;11:1236. [PMID: 32144248 DOI: 10.1038/s41467-020-15027-z] [Cited by in Crossref: 31] [Cited by in F6Publishing: 40] [Article Influence: 15.5] [Reference Citation Analysis]
67 Kaissis G, Braren R. Pancreatic cancer detection and characterization-state of the art cross-sectional imaging and imaging data analysis. Transl Gastroenterol Hepatol 2019;4:35. [PMID: 31231702 DOI: 10.21037/tgh.2019.05.04] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 2.7] [Reference Citation Analysis]
68 Wang Y, Chen S, Shi F, Cheng X, Xu Q, Li J, Luo S, Jiang P, Wei Y, Zhou C, Zheng L, Xia K, Lu G, Zhang Z. MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst. Comput Math Methods Med 2021;2021:6438861. [PMID: 34422095 DOI: 10.1155/2021/6438861] [Reference Citation Analysis]
69 Wang C, Chen X, Luo H, Liu Y, Meng R, Wang M, Liu S, Xu G, Ren J, Zhou P. Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors. Front Oncol 2021;11:754843. [PMID: 34820327 DOI: 10.3389/fonc.2021.754843] [Reference Citation Analysis]
70 Shaish H, Aukerman A, Vanguri R, Spinelli A, Armenta P, Jambawalikar S, Makkar J, Bentley-Hibbert S, Del Portillo A, Kiran R, Monti L, Bonifacio C, Kirienko M, Gardner KL, Schwartz L, Keller D. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.Eur Radiol. 2020;30:6263-6273. [PMID: 32500192 DOI: 10.1007/s00330-020-06968-6] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
71 Sardanelli F, Alì M, Hunink MG, Houssami N, Sconfienza LM, Di Leo G. To share or not to share? Expected pros and cons of data sharing in radiological research. Eur Radiol 2018;28:2328-35. [PMID: 29349697 DOI: 10.1007/s00330-017-5165-5] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 3.8] [Reference Citation Analysis]
72 Jiang Y, Li W, Huang C, Tian C, Chen Q, Zeng X, Cao Y, Chen Y, Yang Y, Liu H, Bo Y, Luo C, Li Y, Zhang T, Wang R. Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation. Front Oncol 2020;10:909. [PMID: 32850304 DOI: 10.3389/fonc.2020.00909] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
73 Nyflot MJ, Thammasorn P, Wootton LS, Ford EC, Chaovalitwongse WA. Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks. Med Phys. 2019;46:456-464. [PMID: 30548601 DOI: 10.1002/mp.13338] [Cited by in Crossref: 38] [Cited by in F6Publishing: 34] [Article Influence: 9.5] [Reference Citation Analysis]
74 Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021;13:2522. [PMID: 34063937 DOI: 10.3390/cancers13112522] [Reference Citation Analysis]
75 Qian WL, Jiang Y, Liu X, Guo YK, Li Y, Tang X, Yang ZG. Distinguishing cardiac myxomas from cardiac thrombi by a radiomics signature based on cardiovascular contrast-enhanced computed tomography images. BMC Cardiovasc Disord 2021;21:152. [PMID: 33765929 DOI: 10.1186/s12872-021-01961-3] [Reference Citation Analysis]
76 Nasief H, Zheng C, Schott D, Hall W, Tsai S, Erickson B, Allen Li X. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precis Oncol 2019;3:25. [PMID: 31602401 DOI: 10.1038/s41698-019-0096-z] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 6.7] [Reference Citation Analysis]
77 Zhou B, Xu J, Tian Y, Yuan S, Li X. Correlation between radiomic features based on contrast-enhanced computed tomography images and Ki-67 proliferation index in lung cancer: A preliminary study. Thorac Cancer 2018;9:1235-40. [PMID: 30070037 DOI: 10.1111/1759-7714.12821] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]
78 Gitto S, Cuocolo R, Albano D, Chianca V, Messina C, Gambino A, Ugga L, Cortese MC, Lazzara A, Ricci D, Spairani R, Zanchetta E, Luzzati A, Brunetti A, Parafioriti A, Sconfienza LM. MRI radiomics-based machine-learning classification of bone chondrosarcoma. Eur J Radiol. 2020;128:109043. [PMID: 32438261 DOI: 10.1016/j.ejrad.2020.109043] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
79 Giraudo C, Kainberger F, Boesen M, Trattnig S. Quantitative Imaging in Inflammatory Arthritis: Between Tradition and Innovation. Semin Musculoskelet Radiol 2020;24:337-54. [PMID: 32992363 DOI: 10.1055/s-0040-1708823] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
80 Zhang W, Chen T, Zhang M, Liu P, Lu Z. [A radiomics-based model for differentiation between benign and malignant gastrointestinal stromal tumors]. Nan Fang Yi Ke Da Xue Xue Bao 2018;38:55-61. [PMID: 33177032 DOI: 10.3969/j.issn.1673-4254.2018.01.09] [Reference Citation Analysis]
81 Schick U, Lucia F, Dissaux G, Visvikis D, Badic B, Masson I, Pradier O, Bourbonne V, Hatt M. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology. Br J Radiol 2019;92:20190105. [PMID: 31538516 DOI: 10.1259/bjr.20190105] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 6.3] [Reference Citation Analysis]
82 Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA 2nd, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021;16:5309-38. [PMID: 34552262 DOI: 10.1038/s41596-021-00617-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
83 Steiger P, Sood R. How Can Radiomics Be Consistently Applied across Imagers and Institutions? Radiology 2019;291:60-1. [PMID: 30694167 DOI: 10.1148/radiol.2019190051] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.7] [Reference Citation Analysis]
84 Bailly C, Bodet-Milin C, Bourgeois M, Gouard S, Ansquer C, Barbaud M, Sébille JC, Chérel M, Kraeber-Bodéré F, Carlier T. Exploring Tumor Heterogeneity Using PET Imaging: The Big Picture. Cancers (Basel) 2019;11:E1282. [PMID: 31480470 DOI: 10.3390/cancers11091282] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 5.7] [Reference Citation Analysis]
85 Xie H, Ma S, Guo X, Zhang X, Wang X. Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model. Eur J Radiol. 2020;122:108747. [PMID: 31760275 DOI: 10.1016/j.ejrad.2019.108747] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
86 Ye DM, Wang HT, Yu T. The Application of Radiomics in Breast MRI: A Review. Technol Cancer Res Treat 2020;19:1533033820916191. [PMID: 32347167 DOI: 10.1177/1533033820916191] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
87 Lu H, Yuan Y, Zhou Z, Ma X, Shen F, Xia Y, Lu J. Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods. Biomed Res Int 2021;2021:5566885. [PMID: 34337027 DOI: 10.1155/2021/5566885] [Reference Citation Analysis]
88 Purkayastha S, Zhao Y, Wu J, Hu R, McGirr A, Singh S, Chang K, Huang RY, Zhang PJ, Silva A, Soulen MC, Stavropoulos SW, Zhang Z, Bai HX. Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm. Sci Rep 2020;10:19503. [PMID: 33177576 DOI: 10.1038/s41598-020-76132-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
89 Manganaro L, Nicolino GM, Dolciami M, Martorana F, Stathis A, Colombo I, Rizzo S. Radiomics in cervical and endometrial cancer. Br J Radiol 2021;94:20201314. [PMID: 34233456 DOI: 10.1259/bjr.20201314] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
90 Castaldo R, Pane K, Nicolai E, Salvatore M, Franzese M. The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status. Cancers (Basel) 2020;12:E518. [PMID: 32102334 DOI: 10.3390/cancers12020518] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 6.5] [Reference Citation Analysis]
91 Yip SSF, Liu Y, Parmar C, Li Q, Liu S, Qu F, Ye Z, Gillies RJ, Aerts HJWL. Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep 2017;7:3519. [PMID: 28615677 DOI: 10.1038/s41598-017-02425-5] [Cited by in Crossref: 46] [Cited by in F6Publishing: 41] [Article Influence: 9.2] [Reference Citation Analysis]
92 Hamerla G, Meyer H, Schob S, Ginat DT, Altman A, Lim T, Gihr GA, Horvath-rizea D, Hoffmann K, Surov A. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study. Magnetic Resonance Imaging 2019;63:244-9. [DOI: 10.1016/j.mri.2019.08.011] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 6.3] [Reference Citation Analysis]
93 Dregely I, Prezzi D, Kelly-Morland C, Roccia E, Neji R, Goh V. Imaging biomarkers in oncology: Basics and application to MRI. J Magn Reson Imaging 2018;48:13-26. [PMID: 29969192 DOI: 10.1002/jmri.26058] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
94 Wang X, Li X, Chen H, Peng Y, Li Y. Pulmonary MRI Radiomics and Machine Learning: Effect of Intralesional Heterogeneity on Classification of Lesion. Acad Radiol 2021:S1076-6332(21)00006-4. [PMID: 33495072 DOI: 10.1016/j.acra.2020.12.020] [Reference Citation Analysis]
95 Peng Y, Lin P, Wu L, Wan D, Zhao Y, Liang L, Ma X, Qin H, Liu Y, Li X, Wang X, He Y, Yang H. Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer. Front Oncol 2020;10:1646. [PMID: 33072550 DOI: 10.3389/fonc.2020.01646] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
96 Gitto S, Cuocolo R, Emili I, Tofanelli L, Chianca V, Albano D, Messina C, Imbriaco M, Sconfienza LM. Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors. J Digit Imaging 2021;34:820-32. [PMID: 34405298 DOI: 10.1007/s10278-021-00498-3] [Reference Citation Analysis]
97 Forghani R. An update on advanced dual-energy CT for head and neck cancer imaging. Expert Rev Anticancer Ther 2019;19:633-44. [PMID: 31177872 DOI: 10.1080/14737140.2019.1626234] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
98 Li Z, Li Q, Song B, Chen Y, Sun Q, Xie Y, Wang L. Clustering of MRI Radiomics Features for Glioblastoma Multiforme: An Initial Study. In: Zheng G, Liao H, Jannin P, Cattin P, Lee S, editors. Medical Imaging and Augmented Reality. Cham: Springer International Publishing; 2016. pp. 311-9. [DOI: 10.1007/978-3-319-43775-0_28] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
99 van Timmeren JE, Leijenaar RT, van Elmpt W, Reymen B, Oberije C, Monshouwer R, Bussink J, Brink C, Hansen O, Lambin P. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiotherapy and Oncology 2017;123:363-9. [DOI: 10.1016/j.radonc.2017.04.016] [Cited by in Crossref: 83] [Cited by in F6Publishing: 76] [Article Influence: 16.6] [Reference Citation Analysis]
100 Zhang Z, He K, Wang Z, Zhang Y, Wu D, Zeng L, Zeng J, Ye Y, Gu T, Xiao X. Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study. Front Oncol 2021;11:779202. [PMID: 34869030 DOI: 10.3389/fonc.2021.779202] [Reference Citation Analysis]
101 Zhang L, Chen Z, Feng L, Guo L, Liu D, Hai J, Qiao K, Chen J, Yan B, Cheng G. Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy. BMC Med Imaging 2021;21:115. [PMID: 34301205 DOI: 10.1186/s12880-021-00647-8] [Reference Citation Analysis]
102 Bianchi J, Gonçalves JR, de Oliveira Ruellas AC, Ashman LM, Vimort JB, Yatabe M, Paniagua B, Hernandez P, Benavides E, Soki FN, Ioshida M, Cevidanes LHS. Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis. Int J Oral Maxillofac Surg 2021;50:227-35. [PMID: 32605824 DOI: 10.1016/j.ijom.2020.04.018] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
103 Chen Y, Chen TW, Wu CQ, Lin Q, Hu R, Xie CL, Zuo HD, Wu JL, Mu QW, Fu QS, Yang GQ, Zhang XM. Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol 2019;29:4408-17. [PMID: 30413966 DOI: 10.1007/s00330-018-5824-1] [Cited by in Crossref: 22] [Cited by in F6Publishing: 23] [Article Influence: 5.5] [Reference Citation Analysis]
104 Jha AK, Mithun S, Rangarajan V, Wee L, Dekker A. Emerging role of artificial intelligence in nuclear medicine. Nucl Med Commun 2021;42:592-601. [PMID: 33660696 DOI: 10.1097/MNM.0000000000001381] [Reference Citation Analysis]
105 Noortman WA, Vriens D, Grootjans W, Tao Q, de Geus-Oei LF, Van Velden FH. Nuclear medicine radiomics in precision medicine: why we can't do without artificial intelligence. Q J Nucl Med Mol Imaging 2020;64:278-90. [PMID: 32397702 DOI: 10.23736/S1824-4785.20.03263-X] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
106 Wei R, Wang H, Wang L, Hu W, Sun X, Dai Z, Zhu J, Li H, Ge Y, Song B. Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer. BMC Med Imaging 2021;21:20. [PMID: 33563233 DOI: 10.1186/s12880-021-00553-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
107 Lawell MP, Indelicato DJ, Paulino AC, Hartsell W, Laack NN, Ermoian RP, Perentesis JP, Vatner R, Perkins S, Mangona VS, Hill-Kayser CE, Wolden SL, Kwok Y, Chang JH, Wilkinson JB, MacEwan I, Chang AL, Eaton BR, Ladra MM, Gallotto SL, Weyman EA, Bajaj BVM, Baliga S, Yeap BY, Berrington de Gonzalez A, Yock TI. An open invitation to join the Pediatric Proton/Photon Consortium Registry to standardize data collection in pediatric radiation oncology. Br J Radiol 2020;93:20190673. [PMID: 31600082 DOI: 10.1259/bjr.20190673] [Cited by in Crossref: 12] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
108 Park JE, Kim HS, Kim D, Park SY, Kim JY, Cho SJ, Kim JH. A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features. BMC Cancer 2020;20:29. [PMID: 31924170 DOI: 10.1186/s12885-019-6504-5] [Cited by in Crossref: 24] [Cited by in F6Publishing: 19] [Article Influence: 12.0] [Reference Citation Analysis]
109 Song W, Yu X, Guo D, Liu H, Tang Z, Liu X, Zhou J, Zhang H, Liu Y. MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization.J Magn Reson Imaging. 2020;52:461-473. [PMID: 31675174 DOI: 10.1002/jmri.26977] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
110 Lin G, Keshari KR, Park JM. Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy. Contrast Media Mol Imaging 2017;2017:6053879. [PMID: 29114178 DOI: 10.1155/2017/6053879] [Cited by in Crossref: 21] [Cited by in F6Publishing: 16] [Article Influence: 4.2] [Reference Citation Analysis]
111 Kocak B, Durmaz ES, Kaya OK, Ates E, Kilickesmez O. Reliability of Single-Slice-Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility. AJR Am J Roentgenol 2019;213:377-83. [PMID: 31063427 DOI: 10.2214/AJR.19.21212] [Cited by in Crossref: 17] [Cited by in F6Publishing: 7] [Article Influence: 5.7] [Reference Citation Analysis]
112 Li M, Wang H, Shang Z, Yang Z, Zhang Y, Wan H. Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning. J Clin Neurosci 2020;78:175-80. [PMID: 32336636 DOI: 10.1016/j.jocn.2020.04.080] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
113 Li J, Yang Z, Xin B, Hao Y, Wang L, Song S, Xu J, Wang X. Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics. Front Oncol 2021;11:702055. [PMID: 34367985 DOI: 10.3389/fonc.2021.702055] [Reference Citation Analysis]
114 Xu X, Huang L, Chen J, Wen J, Liu D, Cao J, Wang J, Fan M. Application of radiomics signature captured from pretreatment thoracic CT to predict brain metastases in stage III/IV ALK-positive non-small cell lung cancer patients. J Thorac Dis 2019;11:4516-28. [PMID: 31903240 DOI: 10.21037/jtd.2019.11.01] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 0.7] [Reference Citation Analysis]
115 Bandara MS, Gurunayaka B, Lakraj G, Pallewatte A, Siribaddana S, Wansapura J. Ultrasound Based Radiomics Features of Chronic Kidney Disease. Acad Radiol 2021:S1076-6332(21)00012-X. [PMID: 33589307 DOI: 10.1016/j.acra.2021.01.006] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
116 Kocak B, Kus EA, Kilickesmez O. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. Eur Radiol 2021;31:1819-30. [PMID: 33006018 DOI: 10.1007/s00330-020-07324-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
117 Yaffe MJ. Emergence of "Big Data" and Its Potential and Current Limitations in Medical Imaging. Semin Nucl Med 2019;49:94-104. [PMID: 30819400 DOI: 10.1053/j.semnuclmed.2018.11.010] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
118 Stieb S, Kiser K, van Dijk L, Livingstone NR, Elhalawani H, Elgohari B, McDonald B, Ventura J, Mohamed ASR, Fuller CD. Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques. Hematol Oncol Clin North Am 2020;34:293-306. [PMID: 31739950 DOI: 10.1016/j.hoc.2019.09.010] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
119 Foy JJ, Shenouda M, Ramahi S, Armato S, Ginat DT. Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features. J Med Imaging (Bellingham) 2020;7:064007. [PMID: 33409336 DOI: 10.1117/1.JMI.7.6.064007] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
120 Ibrahim A, Widaatalla Y, Refaee T, Primakov S, Miclea RL, Öcal O, Fabritius MP, Ingrisch M, Ricke J, Hustinx R, Mottaghy FM, Woodruff HC, Seidensticker M, Lambin P. Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data. Cancers (Basel) 2021;13:4638. [PMID: 34572870 DOI: 10.3390/cancers13184638] [Reference Citation Analysis]
121 Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, Ye Z, Makrigiorgos M, Fennessy F, Mak RH, Gillies R, Quackenbush J, Aerts HJWL. Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. Cancer Res 2017;77:3922-30. [PMID: 28566328 DOI: 10.1158/0008-5472.CAN-17-0122] [Cited by in Crossref: 143] [Cited by in F6Publishing: 95] [Article Influence: 28.6] [Reference Citation Analysis]
122 Zhang T, Dong X, Zhou Y, Liu M, Hang J, Wu L. Development and validation of a radiomics nomogram to discriminate advanced pancreatic cancer with liver metastases or other metastatic patterns. Cancer Biomark 2021;32:541-50. [PMID: 34334383 DOI: 10.3233/CBM-210190] [Reference Citation Analysis]
123 Kesner A, Laforest R, Otazo R, Jennifer K, Pan T. Medical imaging data in the digital innovation age. Med Phys 2018;45:e40-52. [PMID: 29405298 DOI: 10.1002/mp.12794] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
124 Han L, Zhu Y, Liu Z, Yu T, He C, Jiang W, Kan Y, Dong D, Tian J, Luo Y. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol. 2019;29:3820-3829. [PMID: 30701328 DOI: 10.1007/s00330-018-5981-2] [Cited by in Crossref: 42] [Cited by in F6Publishing: 39] [Article Influence: 14.0] [Reference Citation Analysis]
125 Westcott A, Capaldi DPI, McCormack DG, Ward AD, Fenster A, Parraga G. Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation. Radiology 2019;293:676-84. [PMID: 31638491 DOI: 10.1148/radiol.2019190450] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
126 Katsoulakis E, Yu Y, Apte AP, Leeman JE, Katabi N, Morris L, Deasy JO, Chan TA, Lee NY, Riaz N, Hatzoglou V, Oh JH. Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma. Oral Oncol 2020;110:104877. [PMID: 32619927 DOI: 10.1016/j.oraloncology.2020.104877] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
127 Huang ZS, Xiao X, Li XD, Mo HZ, He WL, Deng YH, Lu LJ, Wu YK, Liu H. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma. J Magn Reson Imaging 2021. [PMID: 34085336 DOI: 10.1002/jmri.27761] [Reference Citation Analysis]
128 Galati F, Trimboli RM, Pediconi F. Special Issue "Advances in Breast MRI". Diagnostics (Basel) 2021;11:2297. [PMID: 34943534 DOI: 10.3390/diagnostics11122297] [Reference Citation Analysis]
129 Bologna M, Corino V, Mainardi L. Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. Med Phys 2019;46:5116-23. [PMID: 31539450 DOI: 10.1002/mp.13834] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 5.3] [Reference Citation Analysis]
130 Liu A, Wang Z, Yang Y, Wang J, Dai X, Wang L, Lu Y, Xue F. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun (Lond) 2020;40:16-24. [PMID: 32125097 DOI: 10.1002/cac2.12002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
131 Wolsztynski E, O'Sullivan F, Keyes E, O'Sullivan J, Eary JF. Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma. J Med Imaging (Bellingham) 2018;5:024502. [PMID: 29845091 DOI: 10.1117/1.JMI.5.2.024502] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
132 Izumiya M. Editorial for "Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors". J Magn Reson Imaging 2020;52:1137-8. [PMID: 32614118 DOI: 10.1002/jmri.27280] [Reference Citation Analysis]
133 Konert T, Everitt S, La Fontaine MD, van de Kamer JB, MacManus MP, Vogel WV, Callahan J, Sonke JJ. Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any? PLoS One 2020;15:e0228793. [PMID: 32097418 DOI: 10.1371/journal.pone.0228793] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
134 Moreno S, Bonfante M, Zurek E, Cherezov D, Goldgof D, Hall L, Schabath M. A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC. Tomography 2021;7:154-68. [PMID: 33946756 DOI: 10.3390/tomography7020014] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
135 Zhao W, Huang X, Wang G, Guo J. PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma. Cancer Imaging 2022;22:7. [PMID: 35022071 DOI: 10.1186/s40644-021-00438-y] [Reference Citation Analysis]
136 Ninatti G, Kirienko M, Neri E, Sollini M, Chiti A. Imaging-Based Prediction of Molecular Therapy Targets in NSCLC by Radiogenomics and AI Approaches: A Systematic Review. Diagnostics (Basel) 2020;10:E359. [PMID: 32486314 DOI: 10.3390/diagnostics10060359] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
137 Liu C, Qiao M, Jiang F, Guo Y, Jin Z, Wang Y. TN-USMA Net: Triple normalization-based gastrointestinal stromal tumors classification on multicenter EUS images with ultrasound-specific pretraining and meta attention. Med Phys 2021;48:7199-214. [PMID: 34412155 DOI: 10.1002/mp.15172] [Reference Citation Analysis]
138 Kha QH, Le VH, Hung TNK, Le NQK. Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas. Cancers (Basel) 2021;13:5398. [PMID: 34771562 DOI: 10.3390/cancers13215398] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
139 Jin J, Zhu H, Zhang J, Ai Y, Zhang J, Teng Y, Xie C, Jin X. Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer. Front Oncol 2020;10:614201. [PMID: 33680934 DOI: 10.3389/fonc.2020.614201] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
140 Hou Z, Yang Y, Li S, Yan J, Ren W, Liu J, Wang K, Liu B, Wan S. Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis. Quant Imaging Med Surg 2018;8:410-20. [PMID: 29928606 DOI: 10.21037/qims.2018.05.01] [Cited by in Crossref: 15] [Cited by in F6Publishing: 21] [Article Influence: 3.8] [Reference Citation Analysis]
141 Liang CH, Liu YC, Wan YL, Yun CH, Wu WJ, López-González R, Huang WM. Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images. Cancers (Basel) 2021;13:5600. [PMID: 34830759 DOI: 10.3390/cancers13225600] [Reference Citation Analysis]
142 Zhong J, Hu Y, Si L, Jia G, Xing Y, Zhang H, Yao W. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 2021;31:1526-35. [PMID: 32876837 DOI: 10.1007/s00330-020-07221-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
143 Meng X, Shu J, Xia Y, Yang R. A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma. Biomed Res Int 2020;2020:7103647. [PMID: 32775436 DOI: 10.1155/2020/7103647] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
144 Kocak B, Durmaz ES, Ates E, Sel I, Turgut Gunes S, Kaya OK, Zeynalova A, Kilickesmez O. Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status. Eur Radiol 2020;30:877-86. [DOI: 10.1007/s00330-019-06492-2] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 6.7] [Reference Citation Analysis]
145 Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology. 2019;290:607-618. [PMID: 30667332 DOI: 10.1148/radiol.2018181928] [Cited by in Crossref: 54] [Cited by in F6Publishing: 43] [Article Influence: 18.0] [Reference Citation Analysis]
146 Hammouda K, Khalifa F, Soliman A, Ghazal M, El-Ghar MA, Badawy MA, Darwish HE, Khelifi A, El-Baz A. A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging. Comput Med Imaging Graph 2021;90:101911. [PMID: 33848756 DOI: 10.1016/j.compmedimag.2021.101911] [Reference Citation Analysis]
147 Zhang L, Giuste F, Vizcarra JC, Li X, Gutman D. Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma. Front Oncol 2020;10:937. [PMID: 32676453 DOI: 10.3389/fonc.2020.00937] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
148 Bai H, Xia W, Ji X, He D, Zhao X, Bao J, Zhou J, Wei X, Huang Y, Li Q, Gao X. Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer. J Magn Reson Imaging 2021. [PMID: 33970517 DOI: 10.1002/jmri.27678] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
149 Xie CY, Pang CL, Chan B, Wong EY, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 2021;13:2469. [PMID: 34069367 DOI: 10.3390/cancers13102469] [Reference Citation Analysis]
150 He L, Huang Y, Yan L, Zheng J, Liang C, Liu Z. Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer. Chin J Cancer Res 2019;31:641-52. [PMID: 31564807 DOI: 10.21147/j.issn.1000-9604.2019.04.08] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
151 Pérez-Beteta J, Molina-García D, Villena M, Rodríguez MJ, Velásquez C, Martino J, Meléndez-Asensio B, Rodríguez de Lope Á, Morcillo R, Sepúlveda JM, Hernández-Laín A, Ramos A, Barcia JA, Lara PC, Albillo D, Revert A, Arana E, Pérez-García VM. Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups. AJNR Am J Neuroradiol 2019;40:634-40. [PMID: 30923085 DOI: 10.3174/ajnr.A6019] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
152 Antonelli L, Guarracino MR, Maddalena L, Sangiovanni M. Integrating imaging and omics data: A review. Biomedical Signal Processing and Control 2019;52:264-80. [DOI: 10.1016/j.bspc.2019.04.032] [Cited by in Crossref: 16] [Cited by in F6Publishing: 5] [Article Influence: 5.3] [Reference Citation Analysis]
153 Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27(27): 4395-4412 [PMID: 34366612 DOI: 10.3748/wjg.v27.i27.4395] [Reference Citation Analysis]
154 Robles AI, Harris CC. Integration of multiple "OMIC" biomarkers: A precision medicine strategy for lung cancer. Lung Cancer 2017;107:50-8. [PMID: 27344275 DOI: 10.1016/j.lungcan.2016.06.003] [Cited by in Crossref: 29] [Cited by in F6Publishing: 28] [Article Influence: 4.8] [Reference Citation Analysis]
155 Ingrisch M, Schneider MJ, Nörenberg D, Negrao de Figueiredo G, Maier-Hein K, Suchorska B, Schüller U, Albert N, Brückmann H, Reiser M, Tonn JC, Ertl-Wagner B. Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma. Invest Radiol 2017;52:360-6. [PMID: 28079702 DOI: 10.1097/RLI.0000000000000349] [Cited by in Crossref: 62] [Cited by in F6Publishing: 31] [Article Influence: 15.5] [Reference Citation Analysis]
156 Weng Q, Zhou L, Wang H, Hui J, Chen M, Pang P, Zheng L, Xu M, Wang Z, Ji J. A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules. Clin Radiol 2019;74:933-43. [PMID: 31521324 DOI: 10.1016/j.crad.2019.07.026] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
157 Ming Y, Wu N, Qian T, Li X, Wan DQ, Li C, Li Y, Wu Z, Wang X, Liu J, Wu N. Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer. Front Oncol 2020;10:1301. [PMID: 32903496 DOI: 10.3389/fonc.2020.01301] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
158 Yu AC, Badve C, Ponsky LE, Pahwa S, Dastmalchian S, Rogers M, Jiang Y, Margevicius S, Schluchter M, Tabayoyong W, Abouassaly R, McGivney D, Griswold MA, Gulani V. Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer. Radiology 2017;283:729-38. [PMID: 28187264 DOI: 10.1148/radiol.2017161599] [Cited by in Crossref: 72] [Cited by in F6Publishing: 62] [Article Influence: 14.4] [Reference Citation Analysis]
159 Hassan I, Kotrotsou A, Bakhtiari AS, Thomas GA, Weinberg JS, Kumar AJ, Sawaya R, Luedi MM, Zinn PO, Colen RR. Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity. Sci Rep 2016;6:25295. [PMID: 27151623 DOI: 10.1038/srep25295] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 2.7] [Reference Citation Analysis]
160 Lafata KJ, Hong JC, Geng R, Ackerson BG, Liu J, Zhou Z, Torok J, Kelsey CR, Yin F. Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Phys Med Biol 2019;64:025007. [DOI: 10.1088/1361-6560/aaf5a5] [Cited by in Crossref: 21] [Cited by in F6Publishing: 19] [Article Influence: 7.0] [Reference Citation Analysis]
161 Hanssen O, Lovinfosse P, Weekers L, Hustinx R, Jouret F. [18F-FDG positron emission tomography in non-oncological renal pathology: Current indications and perspectives]. Nephrol Ther 2019;15:430-8. [PMID: 30982747 DOI: 10.1016/j.nephro.2018.11.007] [Reference Citation Analysis]
162 Homayounieh F, Yan P, Digumarthy SR, Kruger U, Wang G, Kalra MK. Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT. Acad Radiol 2021;28:972-9. [PMID: 34217490 DOI: 10.1016/j.acra.2020.09.021] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
163 Shen H, Chen L, Liu K, Zhao K, Li J, Yu L, Ye H, Zhu W. A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes. Quant Imaging Med Surg 2021;11:2918-32. [PMID: 34249623 DOI: 10.21037/qims-20-1182] [Reference Citation Analysis]
164 Zhao Z, Xiao D, Nie C, Zhang H, Jiang X, Jecha AR, Yan P, Zhao H. Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma. Front Oncol 2021;11:709321. [PMID: 34307178 DOI: 10.3389/fonc.2021.709321] [Reference Citation Analysis]
165 El-Haddad G. PET-Based Percutaneous Needle Biopsy. PET Clin 2016;11:333-49. [PMID: 27321036 DOI: 10.1016/j.cpet.2016.02.009] [Cited by in Crossref: 17] [Cited by in F6Publishing: 11] [Article Influence: 2.8] [Reference Citation Analysis]
166 Cai JH, He Y, Zhong XL, Lei H, Wang F, Luo GH, Zhao H, Liu JC. Magnetic Resonance Texture Analysis in Alzheimer's disease. Acad Radiol 2020;27:1774-83. [PMID: 32057617 DOI: 10.1016/j.acra.2020.01.006] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
167 Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, Yang G, Yan X, Zhang YD, Liu XS. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70:1133-1144. [PMID: 30876945 DOI: 10.1016/j.jhep.2019.02.023] [Cited by in Crossref: 121] [Cited by in F6Publishing: 124] [Article Influence: 40.3] [Reference Citation Analysis]
168 Baessler B, Mannil M, Oebel S, Maintz D, Alkadhi H, Manka R. Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images. Radiology 2018;286:103-12. [PMID: 28836886 DOI: 10.1148/radiol.2017170213] [Cited by in Crossref: 79] [Cited by in F6Publishing: 65] [Article Influence: 15.8] [Reference Citation Analysis]
169 Aberle DR. Implementing lung cancer screening: the US experience. Clin Radiol 2017;72:401-6. [PMID: 28069160 DOI: 10.1016/j.crad.2016.12.003] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 2.6] [Reference Citation Analysis]
170 Zhao S, Hou D, Zheng X, Song W, Liu X, Wang S, Zhou L, Tao X, Lv L, Sun Q, Jin Y, Ding L, Mao L, Wu N. MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer. Transl Lung Cancer Res 2021;10:368-80. [PMID: 33569319 DOI: 10.21037/tlcr-20-361] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
171 Abunahel BM, Pontre B, Kumar H, Petrov MS. Pancreas image mining: a systematic review of radiomics. Eur Radiol 2021;31:3447-67. [PMID: 33151391 DOI: 10.1007/s00330-020-07376-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
172 Said D, Hectors SJ, Wilck E, Rosen A, Stocker D, Bane O, Beksaç AT, Lewis S, Badani K, Taouli B. Characterization of solid renal neoplasms using MRI-based quantitative radiomics features. Abdom Radiol 2020;45:2840-50. [DOI: 10.1007/s00261-020-02540-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
173 Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology 2018;287:732-47. [PMID: 29782246 DOI: 10.1148/radiol.2018172171] [Cited by in Crossref: 83] [Cited by in F6Publishing: 71] [Article Influence: 20.8] [Reference Citation Analysis]
174 Park SH, Lim H, Bae BK, Hahm MH, Chong GO, Jeong SY, Kim JC. Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer. Cancer Imaging 2021;21:19. [PMID: 33531073 DOI: 10.1186/s40644-021-00388-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
175 Yan JL, Toh CH, Ko L, Wei KC, Chen PY. A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI. Cancers (Basel) 2021;13:2006. [PMID: 33919447 DOI: 10.3390/cancers13092006] [Reference Citation Analysis]
176 Su X, Chen N, Sun H, Liu Y, Yang X, Wang W, Zhang S, Tan Q, Su J, Gong Q, Yue Q. Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain. Neuro Oncol 2020;22:393-401. [PMID: 31563963 DOI: 10.1093/neuonc/noz184] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 12.0] [Reference Citation Analysis]
177 Luo HB, Liu YY, Wang CH, Qing HM, Wang M, Zhang X, Chen XY, Xu GH, Zhou P, Ren J. Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer. PLoS One 2021;16:e0247074. [PMID: 33647031 DOI: 10.1371/journal.pone.0247074] [Reference Citation Analysis]
178 Cheung BMF, Lau KS, Lee VHF, Leung TW, Kong FS, Luk MY, Yuen KK. Computed tomography-based radiomic model predicts radiological response following stereotactic body radiation therapy in early-stage non-small-cell lung cancer and pulmonary oligo-metastases. Radiat Oncol J 2021;39:254-64. [PMID: 34986546 DOI: 10.3857/roj.2021.00311] [Reference Citation Analysis]
179 Tixier F, Jaouen V, Hognon C, Gallinato O, Colin T, Visvikis D. Evaluation of conventional and deep learning based image harmonization methods in radiomics studies. Phys Med Biol 2021;66. [PMID: 34781280 DOI: 10.1088/1361-6560/ac39e5] [Reference Citation Analysis]
180 Torrado-Carvajal A, Toschi N, Albrecht DS, Chang K, Akeju O, Kim M, Edwards RR, Zhang Y, Hooker JM, Duggento A, Kalpathy-Cramer J, Napadow V, Loggia ML. Thalamic neuroinflammation as a reproducible and discriminating signature for chronic low back pain. Pain 2021;162:1241-9. [PMID: 33065737 DOI: 10.1097/j.pain.0000000000002108] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
181 Reska D, Czajkowski M, Jurczuk K, Boldak C, Kwedlo W, Bauer W, Koszelew J, Kretowski M. Integration of solutions and services for multi-omics data analysis towards personalized medicine. Biocybernetics and Biomedical Engineering 2021;41:1646-63. [DOI: 10.1016/j.bbe.2021.10.005] [Reference Citation Analysis]
182 Eun NL, Kang D, Son EJ, Youk JH, Kim JA, Gweon HM. Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy. Eur Radiol 2021;31:6916-28. [PMID: 33693994 DOI: 10.1007/s00330-021-07816-x] [Reference Citation Analysis]
183 Xu S, Yao Q, Liu G, Jin D, Chen H, Xu J, Li Z, Wu G. Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer. Eur Radiol 2020;30:1804-12. [DOI: 10.1007/s00330-019-06484-2] [Cited by in Crossref: 16] [Cited by in F6Publishing: 9] [Article Influence: 5.3] [Reference Citation Analysis]
184 Bhattarai S, Gupta A, Ali E, Ali M, Riad M, Adhikari P, Mostafa JA. Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome? Cureus 2021;13:e13529. [PMID: 33786236 DOI: 10.7759/cureus.13529] [Reference Citation Analysis]
185 Sharma A, Tarbox L, Kurc T, Bona J, Smith K, Kathiravelu P, Bremer E, Saltz JH, Prior F. PRISM: A Platform for Imaging in Precision Medicine. JCO Clin Cancer Inform 2020;4:491-9. [PMID: 32479186 DOI: 10.1200/CCI.20.00001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
186 Tsougos I, Vamvakas A, Kappas C, Fezoulidis I, Vassiou K. Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis. Comput Math Methods Med 2018;2018:7417126. [PMID: 30344618 DOI: 10.1155/2018/7417126] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
187 Mo X, Wu X, Dong D, Guo B, Liang C, Luo X, Zhang B, Zhang L, Dong Y, Lian Z, Liu J, Pei S, Huang W, Ouyang F, Tian J, Zhang S. Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation. Eur Radiol 2020;30:833-43. [DOI: 10.1007/s00330-019-06452-w] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
188 He D, Wang X, Fu C, Wei X, Bao J, Ji X, Bai H, Xia W, Gao X, Huang Y, Hou J. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging 2021;21:46. [PMID: 34225808 DOI: 10.1186/s40644-021-00414-6] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
189 Lu Z, Wang L, Xia K, Jiang H, Weng X, Jiang J, Wu M. Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps. J Med Syst 2019;43. [DOI: 10.1007/s10916-019-1464-5] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
190 Beaumont J, Acosta O, Devillers A, Palard-Novello X, Chajon E, de Crevoisier R, Castelli J. Voxel-based identification of local recurrence sub-regions from pre-treatment PET/CT for locally advanced head and neck cancers. EJNMMI Res 2019;9:90. [PMID: 31535233 DOI: 10.1186/s13550-019-0556-z] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 4.7] [Reference Citation Analysis]
191 Lippi G, Plebani M. Integrated diagnostics: the future of laboratory medicine? Biochem Med (Zagreb) 2020;30:010501. [PMID: 31839719 DOI: 10.11613/BM.2020.010501] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 3.7] [Reference Citation Analysis]
192 Yoon JH, Sun SH, Xiao M, Yang H, Lu L, Li Y, Schwartz LH, Zhao B. Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies. Tomography 2021;7:877-92. [PMID: 34941646 DOI: 10.3390/tomography7040074] [Reference Citation Analysis]
193 Badano A. "How much realism is needed?" - the wrong question in silico imagers have been asking. Med Phys 2017;44:1607-9. [PMID: 28266047 DOI: 10.1002/mp.12187] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.4] [Reference Citation Analysis]
194 Zheng Y, Chen L, Liu M, Wu J, Yu R, Lv F. Nonenhanced MRI-based radiomics model for preoperative prediction of nonperfused volume ratio for high-intensity focused ultrasound ablation of uterine leiomyomas. Int J Hyperthermia 2021;38:1349-58. [PMID: 34486913 DOI: 10.1080/02656736.2021.1972170] [Reference Citation Analysis]
195 Perrin T, Midya A, Yamashita R, Chakraborty J, Saidon T, Jarnagin WR, Gonen M, Simpson AL, Do RKG. Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging. Abdom Radiol (NY) 2018;43:3271-8. [PMID: 29730738 DOI: 10.1007/s00261-018-1600-6] [Cited by in Crossref: 27] [Cited by in F6Publishing: 21] [Article Influence: 13.5] [Reference Citation Analysis]
196 Horvat N, Hope TA, Pickhardt PJ, Petkovska I. Mucinous rectal cancer: concepts and imaging challenges. Abdom Radiol (NY) 2019;44:3569-80. [PMID: 30993392 DOI: 10.1007/s00261-019-02019-x] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
197 Huang RY, Bi WL, Griffith B, Kaufmann TJ, la Fougère C, Schmidt NO, Tonn JC, Vogelbaum MA, Wen PY, Aldape K, Nassiri F, Zadeh G, Dunn IF; International Consortium on Meningiomas. Imaging and diagnostic advances for intracranial meningiomas. Neuro Oncol 2019;21:i44-61. [PMID: 30649491 DOI: 10.1093/neuonc/noy143] [Cited by in Crossref: 35] [Cited by in F6Publishing: 30] [Article Influence: 17.5] [Reference Citation Analysis]
198 Qian L, Ren J, Liu A, Gao Y, Hao F, Zhao L, Wu H, Niu G. MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes. Eur Radiol 2020;30:5815-25. [PMID: 32535738 DOI: 10.1007/s00330-020-06993-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
199 Lee SL, Ravi A, Morton G, Loblaw A, Tseng CL, Haider M, Murgic J, Nicolae A, Semple M, Chung HT. Changes in ADC and T2-weighted MRI-derived radiomic features in patients treated with focal salvage HDR prostate brachytherapy for local recurrence after previous external-beam radiotherapy. Brachytherapy 2019;18:567-73. [PMID: 31126856 DOI: 10.1016/j.brachy.2019.04.006] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
200 Yi X, Guan X, Zhang Y, Liu L, Long X, Yin H, Wang Z, Li X, Liao W, Chen BT, Zee C. Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: a predictive, preventive and personalized medical approach in adrenal incidentalomas. EPMA J 2018;9:421-9. [PMID: 30538793 DOI: 10.1007/s13167-018-0149-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
201 Chen H, Shi L, Nguyen KNB, Monjazeb AM, Matsukuma KE, Loehfelm TW, Huang H, Qiu J, Rong Y. MRI Radiomics for Prediction of Tumor Response and Downstaging in Rectal Cancer Patients after Preoperative Chemoradiation. Adv Radiat Oncol 2020;5:1286-95. [PMID: 33305090 DOI: 10.1016/j.adro.2020.04.016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
202 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2:230-243. [PMID: 29507784 DOI: 10.1136/svn-2017-000101] [Cited by in Crossref: 692] [Cited by in F6Publishing: 380] [Article Influence: 138.4] [Reference Citation Analysis]
203 Sun Q, Chen Y, Liang C, Zhao Y, Lv X, Zou Y, Yan K, Zheng H, Liang D, Li ZC. Biologic Pathways Underlying Prognostic Radiomics Phenotypes from Paired MRI and RNA Sequencing in Glioblastoma. Radiology 2021;301:654-63. [PMID: 34519578 DOI: 10.1148/radiol.2021203281] [Reference Citation Analysis]
204 Jiang N, Zhong L, Zhang C, Luo X, Zhong P, Li X. Value of Conventional MRI Texture Analysis in the Differential Diagnosis of Phyllodes Tumors and Fibroadenomas of the Breast. Breast Care (Basel) 2021;16:283-90. [PMID: 34248470 DOI: 10.1159/000508456] [Reference Citation Analysis]
205 Chen Q, Xia T, Zhang M, Xia N, Liu J, Yang Y. Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges. Aging Dis 2021;12:143-54. [PMID: 33532134 DOI: 10.14336/AD.2020.0421] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
206 Shi Y, Wahle E, Du Q, Krajewski L, Liang X, Zhou S, Zhang C, Baine M, Zheng D. Associations between Statin/Omega3 Usage and MRI-Based Radiomics Signatures in Prostate Cancer. Diagnostics (Basel) 2021;11:85. [PMID: 33430275 DOI: 10.3390/diagnostics11010085] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
207 Wagner F, Laun FB, Kuder TA, Mlynarska A, Maier F, Faust J, Demberg K, Lindemann L, Rivkin B, Nagel AM, Ladd ME, Maier-Hein K, Bickelhaupt S, Bach M. Temperature and concentration calibration of aqueous polyvinylpyrrolidone (PVP) solutions for isotropic diffusion MRI phantoms. PLoS One 2017;12:e0179276. [PMID: 28628638 DOI: 10.1371/journal.pone.0179276] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 3.4] [Reference Citation Analysis]
208 Wan S, Wei Y, Zhang X, Liu X, Zhang W, He Y, Yuan F, Yao S, Yue Y, Song B. Multiparametric radiomics nomogram may be used for predicting the severity of esophageal varices in cirrhotic patients. Ann Transl Med 2020;8:186. [PMID: 32309333 DOI: 10.21037/atm.2020.01.122] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
209 Tan H, Gan F, Wu Y, Zhou J, Tian J, Lin Y, Wang M. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence. Acad Radiol. 2019;. [PMID: 31879160 DOI: 10.1016/j.acra.2019.11.004] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
210 Huang J, Yao H, Li Y, Dong M, Han C, He L, Huang X, Xia T, Yi Z, Wang H, Zhang Y, He J, Liang C, Liu Z. Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma. Chin J Cancer Res 2021;33:69-78. [PMID: 33707930 DOI: 10.21147/j.issn.1000-9604.2021.01.08] [Reference Citation Analysis]
211 Gutsche R, Scheins J, Kocher M, Bousabarah K, Fink GR, Shah NJ, Langen KJ, Galldiks N, Lohmann P. Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients. Cancers (Basel) 2021;13:647. [PMID: 33562803 DOI: 10.3390/cancers13040647] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
212 Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives. Korean J Radiol 2019;20:1124-37. [PMID: 31270976 DOI: 10.3348/kjr.2018.0070] [Cited by in Crossref: 76] [Cited by in F6Publishing: 67] [Article Influence: 38.0] [Reference Citation Analysis]
213 Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology. Comput Struct Biotechnol J. 2019;17:995-1008. [PMID: 31388413 DOI: 10.1016/j.csbj.2019.07.001] [Cited by in Crossref: 56] [Cited by in F6Publishing: 47] [Article Influence: 18.7] [Reference Citation Analysis]
214 Jakola AS, Zhang Y, Skjulsvik AJ, Solheim O, Bø HK, Berntsen EM, Reinertsen I, Gulati S, Förander P, Brismar TB. Quantitative texture analysis in the prediction of IDH status in low-grade gliomas. Clinical Neurology and Neurosurgery 2018;164:114-20. [DOI: 10.1016/j.clineuro.2017.12.007] [Cited by in Crossref: 38] [Cited by in F6Publishing: 30] [Article Influence: 9.5] [Reference Citation Analysis]
215 Muenzel D, Lo GC, Yu HS, Parakh A, Patino M, Kambadakone A, Rummeny EJ, Sahani DV. Material density iodine images in dual-energy CT: Detection and characterization of hypervascular liver lesions compared to magnetic resonance imaging. Eur J Radiol 2017;95:300-6. [PMID: 28987684 DOI: 10.1016/j.ejrad.2017.08.035] [Cited by in Crossref: 21] [Cited by in F6Publishing: 17] [Article Influence: 4.2] [Reference Citation Analysis]
216 Mukherjee P, Zhou M, Lee E, Schicht A, Balagurunathan Y, Napel S, Gillies R, Wong S, Thieme A, Leung A, Gevaert O. A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data. Nat Mach Intell 2020;2:274-82. [PMID: 33791593 DOI: 10.1038/s42256-020-0173-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
217 Kumar T, Achkar S, Haie-meder C, Chargari C. Curiethérapie guidée par imagerie multimodale : l’exemple du cancer du col utérin. Cancer/Radiothérapie 2019;23:765-72. [DOI: 10.1016/j.canrad.2019.07.145] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
218 Yoon YE, Kim S, Chang HJ. Artificial Intelligence and Echocardiography. J Cardiovasc Imaging 2021;29:193-204. [PMID: 34080347 DOI: 10.4250/jcvi.2021.0039] [Reference Citation Analysis]
219 Divgi CR. Imaging Characterization of Thyroid Nodules. Acad Radiol 2019;26:161-2. [PMID: 30528751 DOI: 10.1016/j.acra.2018.11.007] [Reference Citation Analysis]
220 Zhou Y, Su GY, Hu H, Ge YQ, Si Y, Shen MP, Xu XQ, Wu FY. Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer. Eur Radiol 2020;30:6251-62. [PMID: 32500193 DOI: 10.1007/s00330-020-06866-x] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
221 Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020;146:197-208. [PMID: 32563015 DOI: 10.1016/j.lungcan.2020.05.028] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 9.0] [Reference Citation Analysis]
222 Grippo C, Jagmohan P, Helbich TH, Kapetas P, Clauser P, Baltzer PAT. Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI. Eur J Radiol 2021;138:109630. [PMID: 33744507 DOI: 10.1016/j.ejrad.2021.109630] [Reference Citation Analysis]
223 Pesapane F, Tantrige P, Patella F, Biondetti P, Nicosia L, Ianniello A, Rossi UG, Carrafiello G, Ierardi AM. Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists. Med Oncol 2020;37. [DOI: 10.1007/s12032-020-01368-8] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
224 Sarioglu O, Sarioglu FC, Capar AE, Sokmez DFB, Topkaya P, Belet U. The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy. Eur Radiol 2021;31:6105-15. [PMID: 33559698 DOI: 10.1007/s00330-021-07720-4] [Reference Citation Analysis]
225 Cha KH, Hadjiiski L, Chan HP, Weizer AZ, Alva A, Cohan RH, Caoili EM, Paramagul C, Samala RK. Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning. Sci Rep. 2017;7:8738. [PMID: 28821822 DOI: 10.1038/s41598-017-09315-w] [Cited by in Crossref: 73] [Cited by in F6Publishing: 59] [Article Influence: 14.6] [Reference Citation Analysis]
226 Ren S, Zhao R, Cui W, Qiu W, Guo K, Cao Y, Duan S, Wang Z, Chen R. Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma. Front Oncol 2020;10:1618. [PMID: 32984030 DOI: 10.3389/fonc.2020.01618] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
227 Liu M, Ma X, Shen F, Xia Y, Jia Y, Lu J. MRI-based radiomics nomogram to predict synchronous liver metastasis in primary rectal cancer patients. Cancer Med. 2020;9:5155-5163. [PMID: 32476295 DOI: 10.1002/cam4.3185] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
228 Kido A, Nakamoto Y. Implications of the new FIGO staging and the role of imaging in cervical cancer. Br J Radiol 2021;94:20201342. [PMID: 33989030 DOI: 10.1259/bjr.20201342] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
229 deSouza NM, Achten E, Alberich-Bayarri A, Bamberg F, Boellaard R, Clément O, Fournier L, Gallagher F, Golay X, Heussel CP, Jackson EF, Manniesing R, Mayerhofer ME, Neri E, O'Connor J, Oguz KK, Persson A, Smits M, van Beek EJR, Zech CJ; European Society of Radiology. Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR). Insights Imaging 2019;10:87. [PMID: 31468205 DOI: 10.1186/s13244-019-0764-0] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 4.7] [Reference Citation Analysis]
230 Xu X, Zhang J, Yang K, Wang Q, Chen X, Xu B. Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning. Brain Behav 2021;11:e02085. [PMID: 33624945 DOI: 10.1002/brb3.2085] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
231 Faleiros MC, Nogueira-barbosa MH, Dalto VF, Júnior JRF, Tenório APM, Luppino-assad R, Louzada-junior P, Rangayyan RM, de Azevedo-marques PM. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging. Adv Rheumatol 2020;60. [DOI: 10.1186/s42358-020-00126-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
232 Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B, Burgun A. Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep. 2018;8:12611. [PMID: 30135549 DOI: 10.1038/s41598-018-30657-6] [Cited by in Crossref: 52] [Cited by in F6Publishing: 50] [Article Influence: 13.0] [Reference Citation Analysis]
233 Ai HA, Meier JG, Wendt RE 3rd. HU deviation in lung and bone tissues: Characterization and a corrective strategy. Med Phys 2018;45:2108-18. [PMID: 29574856 DOI: 10.1002/mp.12871] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
234 Brendlin AS, Peisen F, Almansour H, Afat S, Eigentler T, Amaral T, Faby S, Calvarons AF, Nikolaou K, Othman AE. A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma. J Immunother Cancer 2021;9:e003261. [PMID: 34795006 DOI: 10.1136/jitc-2021-003261] [Reference Citation Analysis]
235 Velichko YS, Mozafarykhamseh A, Trabzonlu TA, Zhang Z, Rademaker AW, Yaghmai V. Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis. Acad Radiol 2021;28:e93-e100. [PMID: 32303447 DOI: 10.1016/j.acra.2020.03.004] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
236 Liang W, Shao J, Liu W, Ruan S, Tian W, Zhang X, Wan D, Huang Q, Ding Y, Xiao W. Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models. Front Oncol 2020;10:564307. [PMID: 33123475 DOI: 10.3389/fonc.2020.564307] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
237 Fan M, Wu G, Cheng H, Zhang J, Shao G, Li L. Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients. European Journal of Radiology 2017;94:140-7. [DOI: 10.1016/j.ejrad.2017.06.019] [Cited by in Crossref: 69] [Cited by in F6Publishing: 58] [Article Influence: 13.8] [Reference Citation Analysis]
238 Wang H, Chen H, Duan S, Hao D, Liu J. Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas. J Magn Reson Imaging. 2020;51:791-797. [PMID: 31486565 DOI: 10.1002/jmri.26901] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 5.7] [Reference Citation Analysis]
239 Sun Y, Reynolds H, Wraith D, Williams S, Finnegan ME, Mitchell C, Murphy D, Ebert MA, Haworth A. Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study. Australas Phys Eng Sci Med 2017;40:39-49. [PMID: 28120144 DOI: 10.1007/s13246-016-0515-1] [Cited by in Crossref: 20] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
240 Wong OL, Yuan J, Zhou Y, Yu SK, Cheung KY. Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence. Med Phys 2021;48:1239-49. [PMID: 33370474 DOI: 10.1002/mp.14686] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
241 de Souza Filho EM, Fernandes FA, Portela MGR, Newlands PH, de Carvalho LND, Dos Santos TF, Dos Santos AASMD, Mesquita ET, Seixas FL, Mesquita CT, Gismondi RA. Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging. Front Cardiovasc Med 2021;8:741679. [PMID: 34778403 DOI: 10.3389/fcvm.2021.741679] [Reference Citation Analysis]
242 Zhang B, Lian Z, Zhong L, Zhang X, Dong Y, Chen Q, Zhang L, Mo X, Huang W, Yang W, Zhang S. Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma. BMC Cancer 2020;20:502. [PMID: 32487085 DOI: 10.1186/s12885-020-06957-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
243 Zhang S, Yu M, Chen D, Li P, Tang B, Li J. Role of MRI‑based radiomics in locally advanced rectal cancer (Review). Oncol Rep 2022;47:34. [PMID: 34935061 DOI: 10.3892/or.2021.8245] [Reference Citation Analysis]
244 Liu Y, Wu M, Zhang Y, Luo Y, He S, Wang Y, Chen F, Liu Y, Yang Q, Li Y, Wei H, Zhang H, Jin C, Lu N, Li W, Wang S, Guo Y, Ye Z. Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer. Front Oncol 2021;11:657615. [PMID: 33816314 DOI: 10.3389/fonc.2021.657615] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
245 Huang L, Chen J, Hu W, Xu X, Liu D, Wen J, Lu J, Cao J, Zhang J, Gu Y, Wang J, Fan M. Assessment of a Radiomic Signature Developed in a General NSCLC Cohort for Predicting Overall Survival of ALK-Positive Patients With Different Treatment Types. Clin Lung Cancer 2019;20:e638-51. [PMID: 31375452 DOI: 10.1016/j.cllc.2019.05.005] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
246 Song Y, Zhang J, Zhang YD, Hou Y, Yan X, Wang Y, Zhou M, Yao YF, Yang G. FeAture Explorer (FAE): A tool for developing and comparing radiomics models. PLoS One 2020;15:e0237587. [PMID: 32804986 DOI: 10.1371/journal.pone.0237587] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
247 Hu HT, Shan QY, Chen SL, Li B, Feng ST, Xu EJ, Li X, Long JY, Xie XY, Lu MD, Kuang M, Shen JX, Wang W. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners.Radiol Med. 2020;125:697-705. [PMID: 32200455 DOI: 10.1007/s11547-020-01174-2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
248 DiCenzo D, Quiaoit K, Fatima K, Bhardwaj D, Sannachi L, Gangeh M, Sadeghi-Naini A, Dasgupta A, Kolios MC, Trudeau M, Gandhi S, Eisen A, Wright F, Look Hong N, Sahgal A, Stanisz G, Brezden C, Dinniwell R, Tran WT, Yang W, Curpen B, Czarnota GJ. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study. Cancer Med 2020;9:5798-806. [PMID: 32602222 DOI: 10.1002/cam4.3255] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
249 Huang CY, Lee CC, Yang HC, Lin CJ, Wu HM, Chung WY, Shiau CY, Guo WY, Pan DH, Peng SJ. Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery. J Neurooncol 2020;146:439-49. [PMID: 32020474 DOI: 10.1007/s11060-019-03343-4] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
250 Jiang M, Sun D, Guo Y, Guo Y, Xiao J, Wang L, Yao X. Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result. Acad Radiol 2020;27:171-9. [PMID: 31147234 DOI: 10.1016/j.acra.2019.04.016] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 6.7] [Reference Citation Analysis]
251 Sanvito F, Castellano A, Falini A. Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors. Cancers (Basel) 2021;13:424. [PMID: 33498680 DOI: 10.3390/cancers13030424] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
252 Li Z, Zhai G, Zhang J, Wang Z, Liu G, Wu G, Liang D, Zheng H. Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. Eur Radiol 2019;29:3996-4007. [DOI: 10.1007/s00330-018-5872-6] [Cited by in Crossref: 32] [Cited by in F6Publishing: 31] [Article Influence: 8.0] [Reference Citation Analysis]
253 Haider SP, Zeevi T, Baumeister P, Reichel C, Sharaf K, Forghani R, Kann BH, Judson BL, Prasad ML, Burtness B, Mahajan A, Payabvash S. Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma. Cancers (Basel) 2020;12:E1778. [PMID: 32635216 DOI: 10.3390/cancers12071778] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
254 Dietzel M, Schulz-Wendtland R, Ellmann S, Zoubi R, Wenkel E, Hammon M, Clauser P, Uder M, Runnebaum IB, Baltzer PAT. Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer. Sci Rep 2020;10:3664. [PMID: 32111898 DOI: 10.1038/s41598-020-60393-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
255 Vedantam A, Hassan I, Kotrotsou A, Hassan A, Zinn PO, Viswanathan A, Colen RR. Magnetic Resonance-Based Radiomic Analysis of Radiofrequency Lesion Predicts Outcomes After Percutaneous Cordotomy: A Feasibility Study. Oper Neurosurg (Hagerstown) 2020;18:721-7. [PMID: 31665446 DOI: 10.1093/ons/opz288] [Reference Citation Analysis]
256 Dionísio FCF, Oliveira LS, Hernandes MA, Engel EE, Rangayyan RM, Azevedo-Marques PM, Nogueira-Barbosa MH. Manual and semiautomatic segmentation of bone sarcomas on MRI have high similarity. Braz J Med Biol Res 2020;53:e8962. [PMID: 32022102 DOI: 10.1590/1414-431X20198962] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
257 Garlaschi A, Calabrese M, Zaottini F, Tosto S, Gipponi M, Baccini P, Gallo M, Tagliafico AS. Influence of Tumor Subtype, Radiological Sign and Prognostic Factors on Tumor Size Discrepancies Between Digital Breast Tomosynthesis and Final Histology. Cureus 2019;11:e6046. [PMID: 31803564 DOI: 10.7759/cureus.6046] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
258 Pinkert MA, Salkowski LR, Keely PJ, Hall TJ, Block WF, Eliceiri KW. Review of quantitative multiscale imaging of breast cancer. J Med Imaging (Bellingham) 2018;5:010901. [PMID: 29392158 DOI: 10.1117/1.JMI.5.1.010901] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
259 Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C. Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review. J Magn Reson Imaging 2020;52:54-69. [PMID: 31456318 DOI: 10.1002/jmri.26907] [Cited by in Crossref: 19] [Cited by in F6Publishing: 12] [Article Influence: 6.3] [Reference Citation Analysis]
260 Wang X, Li C, Fang M, Zhang L, Zhong L, Dong D, Tian J, Shan X. Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer. BMC Med Imaging 2021;21:58. [PMID: 33757460 DOI: 10.1186/s12880-021-00587-3] [Reference Citation Analysis]
261 Wang S, Shi J, Ye Z, Dong D, Yu D, Zhou M, Liu Y, Gevaert O, Wang K, Zhu Y, Zhou H, Liu Z, Tian J. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J 2019;53:1800986. [PMID: 30635290 DOI: 10.1183/13993003.00986-2018] [Cited by in Crossref: 101] [Cited by in F6Publishing: 76] [Article Influence: 33.7] [Reference Citation Analysis]
262 Aerts HJWL. Data Science in Radiology: A Path Forward. Clin Cancer Res 2018;24:532-4. [PMID: 29097379 DOI: 10.1158/1078-0432.CCR-17-2804] [Cited by in Crossref: 31] [Cited by in F6Publishing: 12] [Article Influence: 6.2] [Reference Citation Analysis]
263 Yang J, Wang T, Yang L, Wang Y, Li H, Zhou X, Zhao W, Ren J, Li X, Tian J, Huang L. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method. Sci Rep 2019;9:4429. [PMID: 30872652 DOI: 10.1038/s41598-019-40831-z] [Cited by in Crossref: 24] [Cited by in F6Publishing: 25] [Article Influence: 8.0] [Reference Citation Analysis]
264 Feliciani G, Mellini L, Carnevale A, Sarnelli A, Menghi E, Piccinini F, Scarpi E, Loi E, Galeotti R, Giganti M, Parenti GC. The potential role of MR based radiomic biomarkers in the characterization of focal testicular lesions. Sci Rep 2021;11:3456. [PMID: 33568713 DOI: 10.1038/s41598-021-83023-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
265 Ma Z, Fang M, Huang Y, He L, Chen X, Liang C, Huang X, Cheng Z, Dong D, Liang C, Xie J, Tian J, Liu Z. CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. Eur J Radiol. 2017;91:142-147. [PMID: 28629560 DOI: 10.1016/j.ejrad.2017.04.007] [Cited by in Crossref: 49] [Cited by in F6Publishing: 50] [Article Influence: 9.8] [Reference Citation Analysis]
266 Zargari A, Du Y, Heidari M, Thai TC, Gunderson CC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y. Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker. Phys Med Biol 2018;63:155020. [PMID: 30010611 DOI: 10.1088/1361-6560/aad3ab] [Cited by in Crossref: 16] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
267 McGarry SD, Bukowy JD, Iczkowski KA, Unteriner JG, Duvnjak P, Lowman AK, Jacobsohn K, Hohenwalter M, Griffin MO, Barrington AW, Foss HE, Keuter T, Hurrell SL, See WA, Nevalainen MT, Banerjee A, LaViolette PS. Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space. Tomography 2019;5:127-34. [PMID: 30854450 DOI: 10.18383/j.tom.2018.00033] [Cited by in Crossref: 9] [Cited by in F6Publishing: 13] [Article Influence: 4.5] [Reference Citation Analysis]
268 Liu Q, Hu P. Extendable and explainable deep learning for pan-cancer radiogenomics research. Curr Opin Chem Biol 2022;66:102111. [PMID: 34999476 DOI: 10.1016/j.cbpa.2021.102111] [Reference Citation Analysis]
269 Yang S, Wang Y, Shi Y, Yang G, Yan Q, Shen J, Wang Q, Zhang H, Yang S, Shan F, Zhang Z. Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation. Magn Reson Imaging 2022;85:80-6. [PMID: 34666158 DOI: 10.1016/j.mri.2021.10.010] [Reference Citation Analysis]
270 Orlhac F, Nioche C, Klyuzhin I, Rahmim A, Buvat I. Radiomics in PET Imaging:: A Practical Guide for Newcomers. PET Clin 2021;16:597-612. [PMID: 34537132 DOI: 10.1016/j.cpet.2021.06.007] [Reference Citation Analysis]
271 Zheng BH, Liu LZ, Zhang ZZ, Shi JY, Dong LQ, Tian LY, Ding ZB, Ji Y, Rao SX, Zhou J, Fan J, Wang XY, Gao Q. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer. 2018;18:1148. [PMID: 30463529 DOI: 10.1186/s12885-018-5024-z] [Cited by in Crossref: 52] [Cited by in F6Publishing: 52] [Article Influence: 13.0] [Reference Citation Analysis]
272 Galati F, Moffa G, Pediconi F. Breast imaging: Beyond the detection. Eur J Radiol 2022;146:110051. [PMID: 34864426 DOI: 10.1016/j.ejrad.2021.110051] [Reference Citation Analysis]
273 Peng S, Chen L, Tao J, Liu J, Zhu W, Liu H, Yang F. Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer. Diagnostics (Basel) 2021;11:2086. [PMID: 34829433 DOI: 10.3390/diagnostics11112086] [Reference Citation Analysis]
274 Theek B, Opacic T, Möckel D, Schmitz G, Lammers T, Kiessling F. Automated Generation of Reliable Blood Velocity Parameter Maps from Contrast-Enhanced Ultrasound Data. Contrast Media Mol Imaging 2017;2017:2098324. [PMID: 29097912 DOI: 10.1155/2017/2098324] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
275 Osapoetra LO, Dasgupta A, DiCenzo D, Fatima K, Quiaoit K, Saifuddin M, Karam I, Poon I, Husain Z, Tran WT, Sannachi L, Czarnota GJ. Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics. Sci Rep 2021;11:6117. [PMID: 33731738 DOI: 10.1038/s41598-021-85221-6] [Reference Citation Analysis]
276 Guerrisi A, Loi E, Ungania S, Russillo M, Bruzzaniti V, Elia F, Desiderio F, Marconi R, Solivetti FM, Strigari L. Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review. Cancer Med 2020;9:1603-12. [PMID: 31951322 DOI: 10.1002/cam4.2709] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
277 Yu H, Meng X, Chen H, Han X, Fan J, Gao W, Du L, Chen Y, Wang Y, Liu X, Zhang L, Ma G, Yang J. Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer. Front Oncol 2020;10:412. [PMID: 32351879 DOI: 10.3389/fonc.2020.00412] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
278 Santos MK, Ferreira Júnior JR, Wada DT, Tenório APM, Barbosa MHN, Marques PMA. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiol Bras 2019;52:387-96. [PMID: 32047333 DOI: 10.1590/0100-3984.2019.0049] [Cited by in Crossref: 25] [Cited by in F6Publishing: 18] [Article Influence: 8.3] [Reference Citation Analysis]
279 Overhoff D, Kohlmann P, Frydrychowicz A, Gatidis S, Loewe C, Moltz J, Kuhnigk JM, Gutberlet M, Winter H, Völker M, Hahn H, Schoenberg SO; Vorstandskommission Radiomics und Big data:., Vorstand der Deutschen Röntgengesellschaft:., Präsidium der Österreichischen Röntgengesellschaft:. The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies - First Application Examples. Rofo 2021;193:276-88. [PMID: 33242898 DOI: 10.1055/a-1244-2775] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
280 Cao H, Erson-Omay EZ, Günel M, Moliterno J, Fulbright RK. A Quantitative Assessment of Pre-Operative MRI Reports in Glioma Patients: Report Metrics and IDH Prediction Ability. Front Oncol 2020;10:600327. [PMID: 33585216 DOI: 10.3389/fonc.2020.600327] [Reference Citation Analysis]
281 Do QN, Lewis MA, Xi Y, Madhuranthakam AJ, Happe SK, Dashe JS, Lenkinski RE, Khan A, Twickler DM. MRI of the Placenta Accreta Spectrum (PAS) Disorder: Radiomics Analysis Correlates With Surgical and Pathological Outcome. J Magn Reson Imaging 2019;51:936-46. [DOI: 10.1002/jmri.26883] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
282 Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep learning workflow in radiology: a primer. Insights Imaging 2020;11:22. [PMID: 32040647 DOI: 10.1186/s13244-019-0832-5] [Cited by in Crossref: 26] [Cited by in F6Publishing: 16] [Article Influence: 13.0] [Reference Citation Analysis]
283 Chen T, Zhang Z, Tan S, Zhang Y, Wei C, Wang S, Zhao W, Qian X, Zhou Z, Shen J, Dai Y, Hu J. MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI. Front Oncol 2022;11:792456. [DOI: 10.3389/fonc.2021.792456] [Reference Citation Analysis]
284 Shur J, Blackledge M, D'Arcy J, Collins DJ, Bali M, O'Leach M, Koh DM. MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study. Eur Radiol Exp 2021;5:2. [PMID: 33462642 DOI: 10.1186/s41747-020-00199-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
285 Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S, Davatzikos C, Nasrallah MP. Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers (Basel) 2021;13:5921. [PMID: 34885031 DOI: 10.3390/cancers13235921] [Reference Citation Analysis]
286 Yan BC, Li Y, Ma FH, Zhang GF, Feng F, Sun MH, Lin GW, Qiang JW. Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study. Eur Radiol 2021;31:411-22. [PMID: 32749583 DOI: 10.1007/s00330-020-07099-8] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
287 Park JH, Choi BS, Han JH, Kim CY, Cho J, Bae YJ, Sunwoo L, Kim JH. MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer. J Clin Med 2021;10:E237. [PMID: 33440723 DOI: 10.3390/jcm10020237] [Reference Citation Analysis]
288 McGarry SD, Hurrell SL, Iczkowski KA, Hall W, Kaczmarowski AL, Banerjee A, Keuter T, Jacobsohn K, Bukowy JD, Nevalainen MT, Hohenwalter MD, See WA, LaViolette PS. Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer. Int J Radiat Oncol Biol Phys 2018;101:1179-87. [PMID: 29908785 DOI: 10.1016/j.ijrobp.2018.04.044] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 4.8] [Reference Citation Analysis]
289 Meldo A, Utkin L, Kovalev M, Kasimov E. The natural language explanation algorithms for the lung cancer computer-aided diagnosis system. Artif Intell Med 2020;108:101952. [PMID: 32972653 DOI: 10.1016/j.artmed.2020.101952] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
290 Liu H, Zhang C, Wang L, Luo R, Li J, Zheng H, Yin Q, Zhang Z, Duan S, Li X, Wang D. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol. 2019;29:4418-4426. [PMID: 30413955 DOI: 10.1007/s00330-018-5802-7] [Cited by in Crossref: 33] [Cited by in F6Publishing: 33] [Article Influence: 8.3] [Reference Citation Analysis]
291 Maffei N, Manco L, Aluisio G, D'angelo E, Ferrazza P, Vanoni V, Meduri B, Lohr F, Guidi G. Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning. Physica Medica 2021;83:278-86. [DOI: 10.1016/j.ejmp.2021.05.009] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
292 Wang Y, Zhang L, Qi L, Yi X, Li M, Zhou M, Chen D, Xiao Q, Wang C, Pang Y, Xu J, Deng H, Liu L, Guan X. Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. J Oncol 2021;2021:8615450. [PMID: 34671399 DOI: 10.1155/2021/8615450] [Reference Citation Analysis]
293 Feng X, Li T, Song X, Zhu H. Bayesian Scalar on Image Regression With Nonignorable Nonresponse. J Am Stat Assoc 2020;115:1574-97. [PMID: 33627920 DOI: 10.1080/01621459.2019.1686391] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
294 Wang K, Qiao Z, Zhao X, Li X, Wang X, Wu T, Chen Z, Fan D, Chen Q, Ai L. Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model. Eur J Nucl Med Mol Imaging 2020;47:1400-11. [PMID: 31773234 DOI: 10.1007/s00259-019-04604-0] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 5.3] [Reference Citation Analysis]
295 Chen H, He Y, Jia W. Precise hepatectomy in the intelligent digital era. Int J Biol Sci 2020;16:365-73. [PMID: 32015674 DOI: 10.7150/ijbs.39387] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
296 Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M. A New Challenge for Radiologists: Radiomics in Breast Cancer. Biomed Res Int 2018;2018:6120703. [PMID: 30402486 DOI: 10.1155/2018/6120703] [Cited by in Crossref: 29] [Cited by in F6Publishing: 24] [Article Influence: 7.3] [Reference Citation Analysis]
297 Erdim C, Yardimci AH, Bektas CT, Kocak B, Koca SB, Demir H, Kilickesmez O. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis. Acad Radiol 2020;27:1422-9. [PMID: 32014404 DOI: 10.1016/j.acra.2019.12.015] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
298 Gu H, Liang H, Zhong J, Wei Y, Ma Y. How does the pancreatic solid pseudopapillary neoplasm confuse us: Analyzing from the point view of MRI-based radiomics? Magn Reson Imaging 2022;85:38-43. [PMID: 34687847 DOI: 10.1016/j.mri.2021.10.034] [Reference Citation Analysis]
299 Bagante F, Tripepi M, Spolverato G, Tsilimigras DI, Pawlik TM. Assessing prognosis in cholangiocarcinoma: a review of promising genetic markers and imaging approaches. Expert Opinion on Orphan Drugs 2020;8:357-65. [DOI: 10.1080/21678707.2020.1801410] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
300 Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021;11:633176. [PMID: 33854969 DOI: 10.3389/fonc.2021.633176] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
301 Zhou P, Zeng R, Yu L, Feng Y, Chen C, Li F, Liu Y, Huang Y, Huang Z; Alzheimer's Disease Neuroimaging Initiative. Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging. Front Aging Neurosci 2021;13:764872. [PMID: 34764864 DOI: 10.3389/fnagi.2021.764872] [Reference Citation Analysis]
302 Papp L, Pötsch N, Grahovac M, Schmidbauer V, Woehrer A, Preusser M, Mitterhauser M, Kiesel B, Wadsak W, Beyer T, Hacker M, Traub-weidinger T. Glioma Survival Prediction with Combined Analysis of In Vivo 11 C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning. J Nucl Med 2018;59:892-9. [DOI: 10.2967/jnumed.117.202267] [Cited by in Crossref: 44] [Cited by in F6Publishing: 42] [Article Influence: 8.8] [Reference Citation Analysis]
303 Li Y, Jiang J, Lu J, Jiang J, Zhang H, Zuo C. Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. Ther Adv Neurol Disord 2019;12:1756286419838682. [PMID: 30956687 DOI: 10.1177/1756286419838682] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 4.0] [Reference Citation Analysis]
304 Li X, Jiang F, Guo Y, Jin Z, Wang Y. Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image. Int J Comput Assist Radiol Surg. 2019;14:1635-1645. [PMID: 31049803 DOI: 10.1007/s11548-019-01993-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
305 Rundo L, Tangherloni A, Cazzaniga P, Nobile MS, Russo G, Gilardi MC, Vitabile S, Mauri G, Besozzi D, Militello C. A novel framework for MR image segmentation and quantification by using MedGA. Computer Methods and Programs in Biomedicine 2019;176:159-72. [DOI: 10.1016/j.cmpb.2019.04.016] [Cited by in Crossref: 29] [Cited by in F6Publishing: 7] [Article Influence: 9.7] [Reference Citation Analysis]
306 Laudicella R, Bauckneht M, Cuppari L, Donegani MI, Arnone A, Baldari S, Burger IA, Quartuccio N; Young Italian Association of Nuclear Medicine (AIMN) Group. Emerging applications of imaging in glioma: focus on PET/MRI and radiomics. Clin Transl Imaging 2021;9:609-23. [DOI: 10.1007/s40336-021-00464-7] [Reference Citation Analysis]
307 Liebgott A, Küstner T, Strohmeier H, Hepp T, Mangold P, Martirosian P, Bamberg F, Nikolaou K, Yang B, Gatidis S. ImFEATbox: a toolbox for extraction and analysis of medical image features. Int J Comput Assist Radiol Surg 2018;13:1881-93. [PMID: 30229363 DOI: 10.1007/s11548-018-1859-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]
308 Park CJ, Han K, Kim H, Ahn SS, Choi D, Park YW, Chang JH, Kim SH, Cha S, Lee SK. MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas. AJNR Am J Neuroradiol 2021;42:448-56. [PMID: 33509914 DOI: 10.3174/ajnr.A6983] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
309 Tchelebi LT, Romesser PB, Feuerlein S, Hoffe S, Latifi K, Felder S, Chuong MD. Magnetic Resonance Guided Radiotherapy for Rectal Cancer: Expanding Opportunities for Non-Operative Management. Cancer Control 2020;27:1073274820969449. [PMID: 33118384 DOI: 10.1177/1073274820969449] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
310 Wang W, Zhang JC, Tian WS, Chen LD, Zheng Q, Hu HT, Wu SS, Guo Y, Xie XY, Lu MD, Kuang M, Liu LZ, Ruan SM. Shear wave elastography-based ultrasomics: differentiating malignant from benign focal liver lesions. Abdom Radiol (NY) 2021;46:237-48. [PMID: 32564210 DOI: 10.1007/s00261-020-02614-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
311 Lombardo E, Kurz C, Marschner S, Avanzo M, Gagliardi V, Fanetti G, Franchin G, Stancanello J, Corradini S, Niyazi M, Belka C, Parodi K, Riboldi M, Landry G. Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts. Sci Rep 2021;11:6418. [PMID: 33742070 DOI: 10.1038/s41598-021-85671-y] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
312 Bae H, Lee H, Kim S, Han K, Rhee H, Kim DK, Kwon H, Hong H, Lim JS. Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists. Eur Radiol 2021. [PMID: 33970307 DOI: 10.1007/s00330-021-07877-y] [Reference Citation Analysis]
313 Alis D, Yergin M, Asmakutlu O, Topel C, Karaarslan E. The influence of cardiac motion on radiomics features: radiomics features of non-enhanced CMR cine images greatly vary through the cardiac cycle. Eur Radiol 2021;31:2706-15. [PMID: 33051731 DOI: 10.1007/s00330-020-07370-y] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
314 Wu D, Albert M, Soldan A, Pettigrew C, Oishi K, Tomogane Y, Ye C, Ma T, Miller MI, Mori S. Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities. Neuroimage Clin 2019;22:101772. [PMID: 30927606 DOI: 10.1016/j.nicl.2019.101772] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 2.3] [Reference Citation Analysis]
315 Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, Scifo P, Picchio M. State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol 2021;169:103544. [PMID: 34801699 DOI: 10.1016/j.critrevonc.2021.103544] [Reference Citation Analysis]
316 Stefano A, Gioè M, Russo G, Palmucci S, Torrisi SE, Bignardi S, Basile A, Comelli A, Benfante V, Sambataro G, Falsaperla D, Torcitto AG, Attanasio M, Yezzi A, Vancheri C. Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT. Diagnostics (Basel) 2020;10:E306. [PMID: 32429182 DOI: 10.3390/diagnostics10050306] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
317 Carles M, Popp I, Starke MM, Mix M, Urbach H, Schimek-Jasch T, Eckert F, Niyazi M, Baltas D, Grosu AL. FET-PET radiomics in recurrent glioblastoma: prognostic value for outcome after re-irradiation? Radiat Oncol 2021;16:46. [PMID: 33658069 DOI: 10.1186/s13014-020-01744-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
318 Deng Y, Ming B, Zhou T, Wu JL, Chen Y, Liu P, Zhang J, Zhang SY, Chen TW, Zhang XM. Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions. Front Oncol 2021;11:620981. [PMID: 33842325 DOI: 10.3389/fonc.2021.620981] [Reference Citation Analysis]
319 Oreiller V, Andrearczyk V, Jreige M, Boughdad S, Elhalawani H, Castelli J, Vallières M, Zhu S, Xie J, Peng Y, Iantsen A, Hatt M, Yuan Y, Ma J, Yang X, Rao C, Pai S, Ghimire K, Feng X, Naser MA, Fuller CD, Yousefirizi F, Rahmim A, Chen H, Wang L, Prior JO, Depeursinge A. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. Med Image Anal 2021;77:102336. [PMID: 35016077 DOI: 10.1016/j.media.2021.102336] [Reference Citation Analysis]
320 Gitto S, Cuocolo R, van Langevelde K, van de Sande MA, Parafioriti A, Luzzati A, Imbriaco M, Sconfienza LM, Bloem JL. MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones. EBioMedicine 2022;75:103757. [DOI: 10.1016/j.ebiom.2021.103757] [Reference Citation Analysis]
321 Giambelluca D, Cannella R, Vernuccio F, Comelli A, Pavone A, Salvaggio L, Galia M, Midiri M, Lagalla R, Salvaggio G. PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer. Curr Probl Diagn Radiol 2021;50:175-85. [PMID: 31761413 DOI: 10.1067/j.cpradiol.2019.10.009] [Cited by in Crossref: 18] [Cited by in F6Publishing: 15] [Article Influence: 6.0] [Reference Citation Analysis]
322 Liang L, Ding Y, Yu Y, Liu K, Rao S, Ge Y, Zeng M. Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study. BMC Med Imaging 2021;21:75. [PMID: 33902469 DOI: 10.1186/s12880-021-00605-4] [Reference Citation Analysis]
323 Pfaehler E, van Sluis J, Merema BBJ, van Ooijen P, Berendsen RCM, van Velden FHP, Boellaard R. Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts. J Nucl Med 2020;61:469-76. [PMID: 31420497 DOI: 10.2967/jnumed.119.229724] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 6.0] [Reference Citation Analysis]
324 Chen M, Yin F, Yu Y, Zhang H, Wen G. CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma. Cancer Imaging 2021;21:42. [PMID: 34162442 DOI: 10.1186/s40644-021-00412-8] [Reference Citation Analysis]
325 Hu X, Sun X, Hu F, Liu F, Ruan W, Wu T, An R, Lan X. Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy. Eur J Nucl Med Mol Imaging 2021;48:3469-81. [PMID: 33829415 DOI: 10.1007/s00259-021-05325-z] [Reference Citation Analysis]
326 Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol 2017;90:20160642. [PMID: 27885836 DOI: 10.1259/bjr.20160642] [Cited by in Crossref: 59] [Cited by in F6Publishing: 56] [Article Influence: 9.8] [Reference Citation Analysis]
327 Ren S, Zhao R, Zhang J, Guo K, Gu X, Duan S, Wang Z, Chen R. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol 2020;45:1524-33. [DOI: 10.1007/s00261-020-02506-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
328 Barone S, Cannella R, Comelli A, Pellegrino A, Salvaggio G, Stefano A, Vernuccio F. Hybrid descriptive‐inferential method for key feature selection in prostate cancer radiomics. Appl Stochastic Models Bus Ind 2021;37:961-72. [DOI: 10.1002/asmb.2642] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
329 Zhong QZ, Long LH, Liu A, Li CM, Xiu X, Hou XY, Wu QH, Gao H, Xu YG, Zhao T, Wang D, Lin HL, Sha XY, Wang WH, Chen M, Li GF. Radiomics of Multiparametric MRI to Predict Biochemical Recurrence of Localized Prostate Cancer After Radiation Therapy. Front Oncol 2020;10:731. [PMID: 32477949 DOI: 10.3389/fonc.2020.00731] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
330 Larroza A, Materka A, López-Lereu MP, Monmeneu JV, Bodí V, Moratal D. Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. Eur J Radiol 2017;92:78-83. [PMID: 28624024 DOI: 10.1016/j.ejrad.2017.04.024] [Cited by in Crossref: 39] [Cited by in F6Publishing: 33] [Article Influence: 7.8] [Reference Citation Analysis]
331 Xu P, Xue Y, Schoepf UJ, Varga-Szemes A, Griffith J, Yacoub B, Zhou F, Zhou C, Yang Y, Xing W, Zhang L. Radiomics: The Next Frontier of Cardiac Computed Tomography. Circ Cardiovasc Imaging 2021;14:e011747. [PMID: 33722057 DOI: 10.1161/CIRCIMAGING.120.011747] [Reference Citation Analysis]
332 Zhang L, Ye Z, Ruan L, Jiang M. Pretreatment MRI-Derived Radiomics May Evaluate the Response of Different Induction Chemotherapy Regimens in Locally advanced Nasopharyngeal Carcinoma. Acad Radiol 2020;27:1655-64. [PMID: 33004261 DOI: 10.1016/j.acra.2020.09.002] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
333 Alilou M, Orooji M, Beig N, Prasanna P, Rajiah P, Donatelli C, Velcheti V, Rakshit S, Yang M, Jacono F, Gilkeson R, Linden P, Madabhushi A. Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas. Sci Rep 2018;8:15290. [PMID: 30327507 DOI: 10.1038/s41598-018-33473-0] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 2.8] [Reference Citation Analysis]
334 Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1(2): 37-50 [DOI: 10.35712/aig.v1.i2.37] [Reference Citation Analysis]
335 Wang Q, Zhang L, Li S, Sun Z, Wu X, Zhao A, Benkert T, Zhou D, Xue H, Jin Z, Li J. Histogram Analysis Based on Apparent Diffusion Coefficient Maps of Bone Marrow in Multiple Myeloma: An Independent Predictor for High-risk Patients Classified by the Revised International Staging System. Acad Radiol 2021:S1076-6332(21)00318-4. [PMID: 34452820 DOI: 10.1016/j.acra.2021.07.010] [Reference Citation Analysis]
336 Ippolito D, Inchingolo R, Grazioli L, Drago SG, Nardella M, Gatti M, Faletti R. Recent advances in non-invasive magnetic resonance imaging assessment of hepatocellular carcinoma. World J Gastroenterol 2018; 24(23): 2413-2426 [PMID: 29930464 DOI: 10.3748/wjg.v24.i23.2413] [Cited by in CrossRef: 14] [Cited by in F6Publishing: 12] [Article Influence: 3.5] [Reference Citation Analysis]
337 Wang H, Chen X, Liu H, Yu C, He L. [Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children]. Nan Fang Yi Ke Da Xue Xue Bao 2021;41:1569-76. [PMID: 34755674 DOI: 10.12122/j.issn.1673-4254.2021.10.17] [Reference Citation Analysis]
338 Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 2017;4:170117. [PMID: 28872634 DOI: 10.1038/sdata.2017.117] [Cited by in Crossref: 612] [Cited by in F6Publishing: 249] [Article Influence: 122.4] [Reference Citation Analysis]
339 Fayaz M, Torokeldiev N, Turdumamatov S, Qureshi MS, Qureshi MB, Gwak J. An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network. Sensors (Basel) 2021;21:7480. [PMID: 34833556 DOI: 10.3390/s21227480] [Reference Citation Analysis]
340 Zeng C, Zhai T, Chen J, Guo L, Huang B, Guo H, Liu G, Zhuang T, Liu W, Luo T, Wu Y, Peng G, Li D, Chen C. Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy. Radiat Oncol 2021;16:8. [PMID: 33436018 DOI: 10.1186/s13014-020-01699-w] [Reference Citation Analysis]
341 Madan CR. Shape-related characteristics of age-related differences in subcortical structures. Aging & Mental Health 2018;23:800-10. [DOI: 10.1080/13607863.2017.1421613] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
342 Nell E, Ober C, Rendahl A, Forrest L, Lawrence J. Volumetric tumor response assessment is inefficient without overt clinical benefit compared to conventional, manual veterinary response assessment in canine nasal tumors. Vet Radiol Ultrasound 2020;61:592-603. [PMID: 32702179 DOI: 10.1111/vru.12895] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
343 Gillies RJ, Beyer T. PET and MRI: Is the Whole Greater than the Sum of Its Parts? Cancer Res 2016;76:6163-6. [PMID: 27729326 DOI: 10.1158/0008-5472.CAN-16-2121] [Cited by in Crossref: 15] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
344 Li L, Kan X, Zhao Y, Liang B, Ye T, Yang L, Zheng C. Radiomics Signature: A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma. Int J Med Sci 2021;18:2276-84. [PMID: 33967603 DOI: 10.7150/ijms.55510] [Reference Citation Analysis]
345 Ingrisch M, Schöppe F, Paprottka K, Fabritius M, Strobl FF, De Toni EN, Ilhan H, Todica A, Michl M, Paprottka PM. Prediction of 90Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests. J Nucl Med 2018;59:769-73. [PMID: 29146692 DOI: 10.2967/jnumed.117.200758] [Cited by in Crossref: 15] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
346 Tran WT, Jerzak K, Lu F, Klein J, Tabbarah S, Lagree A, Wu T, Rosado-mendez I, Law E, Saednia K, Sadeghi-naini A. Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics. Journal of Medical Imaging and Radiation Sciences 2019;50:S32-41. [DOI: 10.1016/j.jmir.2019.07.010] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 6.7] [Reference Citation Analysis]
347 Bai JH, Hsieh MS, Liao HC, Lin MW, Chen JS. Prediction of pleural invasion using different imaging tools in non-small cell lung cancer. Ann Transl Med 2019;7:33. [PMID: 30854386 DOI: 10.21037/atm.2019.01.15] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
348 Li Y, Ju S, Li X, Zhou YL, Qiang JW. Prediction of minimal hepatic encephalopathy by using an radiomics nomogram in chronic hepatic schistosomiasis patients. PLoS Negl Trop Dis 2021;15:e0009834. [PMID: 34653175 DOI: 10.1371/journal.pntd.0009834] [Reference Citation Analysis]
349 Hectors SJ, Lewis S, Besa C, King MJ, Said D, Putra J, Ward S, Higashi T, Thung S, Yao S, Laface I, Schwartz M, Gnjatic S, Merad M, Hoshida Y, Taouli B. MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma. Eur Radiol. 2020;30:3759-3769. [PMID: 32086577 DOI: 10.1007/s00330-020-06675-2] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 10.0] [Reference Citation Analysis]
350 Li H, Li T, Cai Q, Wang X, Liao Y, Cheng Y, Zhou Q. Development and Validation of a Radiomics Nomogram for Differentiating Mycoplasma Pneumonia and Bacterial Pneumonia. Diagnostics (Basel) 2021;11:1330. [PMID: 34441265 DOI: 10.3390/diagnostics11081330] [Reference Citation Analysis]
351 Maruyama H, Kato N. Advances in ultrasound diagnosis in chronic liver diseases. Clin Mol Hepatol 2019;25:160-7. [PMID: 30773001 DOI: 10.3350/cmh.2018.1013] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
352 Yang B, Ji HS, Zhou CS, Dong H, Ma L, Ge YQ, Zhu CH, Tian JH, Zhang LJ, Zhu H, Lu GM. 18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma. Transl Lung Cancer Res 2020;9:563-74. [PMID: 32676320 DOI: 10.21037/tlcr-19-592] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
353 Kressel HY. Setting Sail: 2017. Radiology 2017;282:4-6. [DOI: 10.1148/radiol.2016162471] [Cited by in Crossref: 35] [Cited by in F6Publishing: 35] [Article Influence: 7.0] [Reference Citation Analysis]
354 Tang J, Yang B, Adams MP, Shenkov NN, Klyuzhin IS, Fotouhi S, Davoodi-bojd E, Lu L, Soltanian-zadeh H, Sossi V, Rahmim A. Artificial Neural Network–Based Prediction of Outcome in Parkinson’s Disease Patients Using DaTscan SPECT Imaging Features. Mol Imaging Biol 2019;21:1165-73. [DOI: 10.1007/s11307-019-01334-5] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 3.3] [Reference Citation Analysis]
355 Monti S, Brancato V, Di Costanzo G, Basso L, Puglia M, Ragozzino A, Salvatore M, Cavaliere C. Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol. Cancers (Basel) 2020;12:E390. [PMID: 32046196 DOI: 10.3390/cancers12020390] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
356 Gourtsoyianni S, Doumou G, Prezzi D, Taylor B, Stirling JJ, Taylor NJ, Siddique M, Cook GJR, Glynne-Jones R, Goh V. Primary Rectal Cancer: Repeatability of Global and Local-Regional MR Imaging Texture Features. Radiology 2017;284:552-61. [PMID: 28481194 DOI: 10.1148/radiol.2017161375] [Cited by in Crossref: 35] [Cited by in F6Publishing: 35] [Article Influence: 7.0] [Reference Citation Analysis]
357 Katsila T, Liontos M, Patrinos GP, Bamias A, Kardamakis D. The New Age of -omics in Urothelial Cancer - Re-wording Its Diagnosis and Treatment. EBioMedicine 2018;28:43-50. [PMID: 29428524 DOI: 10.1016/j.ebiom.2018.01.044] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
358 Foy JJ, Al-Hallaq HA, Grekoski V, Tran T, Guruvadoo K, Armato Iii SG, Sensakovic WF. Harmonization of radiomic feature variability resulting from differences in CT image acquisition and reconstruction: assessment in a cadaveric liver. Phys Med Biol 2020;65:205008. [PMID: 33063693 DOI: 10.1088/1361-6560/abb172] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
359 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
360 Malik N, Geraghty B, Dasgupta A, Maralani PJ, Sandhu M, Detsky J, Tseng CL, Soliman H, Myrehaug S, Husain Z, Perry J, Lau A, Sahgal A, Czarnota GJ. MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region. J Neurooncol 2021;155:181-91. [PMID: 34694564 DOI: 10.1007/s11060-021-03866-9] [Reference Citation Analysis]
361 Akai H, Yasaka K, Kunimatsu A, Nojima M, Kokudo T, Kokudo N, Hasegawa K, Abe O, Ohtomo K, Kiryu S. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagnostic and Interventional Imaging 2018;99:643-51. [DOI: 10.1016/j.diii.2018.05.008] [Cited by in Crossref: 40] [Cited by in F6Publishing: 36] [Article Influence: 10.0] [Reference Citation Analysis]
362 Zhang W, Huang Z, Zhao J, He D, Li M, Yin H, Tian S, Zhang H, Song B. Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer. Ann Transl Med 2021;9:134. [PMID: 33569436 DOI: 10.21037/atm-20-7673] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
363 Marcadent S, Hofmeister J, Preti MG, Martin SP, Van De Ville D, Montet X. Generative Adversarial Networks Improve the Reproducibility and Discriminative Power of Radiomic Features. Radiol Artif Intell 2020;2:e190035. [PMID: 33937823 DOI: 10.1148/ryai.2020190035] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
364 Mendes B, Domingues I, Silva A, Santos J. Prostate Cancer Aggressiveness Prediction Using CT Images. Life (Basel) 2021;11:1164. [PMID: 34833040 DOI: 10.3390/life11111164] [Reference Citation Analysis]
365 Bortolotto C, Lancia A, Stelitano C, Montesano M, Merizzoli E, Agustoni F, Stella G, Preda L, Filippi AR. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther 2021;21:257-66. [PMID: 33216651 DOI: 10.1080/14737140.2021.1852935] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
366 Letourneau-Guillon L, Camirand D, Guilbert F, Forghani R. Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clin N Am 2020;30:e1-e15. [PMID: 33039002 DOI: 10.1016/j.nic.2020.08.008] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
367 Meier A, Veeraraghavan H, Nougaret S, Lakhman Y, Sosa R, Soslow RA, Sutton EJ, Hricak H, Sala E, Vargas HA. Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer. Abdom Radiol (NY) 2019;44:2040-7. [PMID: 30474722 DOI: 10.1007/s00261-018-1840-5] [Cited by in Crossref: 17] [Cited by in F6Publishing: 13] [Article Influence: 8.5] [Reference Citation Analysis]
368 Gao RZ, Wen R, Wen DY, Huang J, Qin H, Li X, Wang XR, He Y, Yang H. Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer. J Ultrasound Med 2021. [PMID: 33615528 DOI: 10.1002/jum.15659] [Reference Citation Analysis]
369 Pérez-beteta J, Molina-garcía D, Ortiz-alhambra JA, Fernández-romero A, Luque B, Arregui E, Calvo M, Borrás JM, Meléndez B, Rodríguez de Lope Á, Moreno de la Presa R, Iglesias Bayo L, Barcia JA, Martino J, Velásquez C, Asenjo B, Benavides M, Herruzo I, Revert A, Arana E, Pérez-garcía VM. Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma. Radiology 2018;288:218-25. [DOI: 10.1148/radiol.2018171051] [Cited by in Crossref: 30] [Cited by in F6Publishing: 30] [Article Influence: 7.5] [Reference Citation Analysis]
370 He T, Fong JN, Moore LW, Ezeana CF, Victor D, Divatia M, Vasquez M, Ghobrial RM, Wong STC. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer. Comput Med Imaging Graph 2021;89:101894. [PMID: 33725579 DOI: 10.1016/j.compmedimag.2021.101894] [Reference Citation Analysis]
371 Rompianesi G, Pegoraro F, Ceresa CD, Montalti R, Troisi RI. Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J Gastroenterol 2022; 28(1): 108-122 [DOI: 10.3748/wjg.v28.i1.108] [Reference Citation Analysis]
372 Li Y, Liu X, Qian Z, Sun Z, Xu K, Wang K, Fan X, Zhang Z, Li S, Wang Y, Jiang T. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature. Eur Radiol 2018;28:2960-8. [DOI: 10.1007/s00330-017-5267-0] [Cited by in Crossref: 37] [Cited by in F6Publishing: 36] [Article Influence: 9.3] [Reference Citation Analysis]
373 Vilgrain V, Raynaud L, Paulatto L, Ronot M. Imaging of liver tumours: What’s new? Liver Int 2020;40:154-9. [DOI: 10.1111/liv.14353] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
374 Zhang P, Feng Z, Cai W, You H, Fan C, Lv W, Min X, Wang L. T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma. Front Oncol 2019;9:1330. [PMID: 31850216 DOI: 10.3389/fonc.2019.01330] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
375 Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J Gastroenterol 2021; 27(25): 3802-3814 [PMID: 34321845 DOI: 10.3748/wjg.v27.i25.3802] [Reference Citation Analysis]
376 Hanson G, Chitnis T, Williams MJ, Gan RW, Julian L, Mace K, Chia J, Wormser D, Martinec M, Astorino T, Leviner N, Maung P, Jan A, Belendiuk K. Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform. JAMIA Open 2022;5:ooab110. [DOI: 10.1093/jamiaopen/ooab110] [Reference Citation Analysis]
377 Sherlock M, Scarsbrook A, Abbas A, Fraser S, Limumpornpetch P, Dineen R, Stewart PM. Adrenal Incidentaloma. Endocr Rev. 2020;41. [PMID: 32266384 DOI: 10.1210/endrev/bnaa008] [Cited by in Crossref: 17] [Cited by in F6Publishing: 11] [Article Influence: 17.0] [Reference Citation Analysis]
378 Edalat-javid M, Shiri I, Hajianfar G, Abdollahi H, Arabi H, Oveisi N, Javadian M, Shamsaei Zafarghandi M, Malek H, Bitarafan-rajabi A, Oveisi M, Zaidi H. Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study. J Nucl Cardiol . [DOI: 10.1007/s12350-020-02109-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
379 Chen W, Zhang T, Xu L, Zhao L, Liu H, Gu LR, Wang DZ, Zhang M. Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading. Front Oncol 2021;11:660509. [PMID: 34150628 DOI: 10.3389/fonc.2021.660509] [Reference Citation Analysis]
380 Zhang Y, Yu S, Zhang L, Kang L. Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method. Front Oncol 2020;10:1121. [PMID: 32850321 DOI: 10.3389/fonc.2020.01121] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
381 Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018;36:257-272. [PMID: 29498017 DOI: 10.1007/s11604-018-0726-3] [Cited by in Crossref: 93] [Cited by in F6Publishing: 68] [Article Influence: 23.3] [Reference Citation Analysis]
382 Cho YH, Chae EJ, Song JW, Do KH, Jang SJ. Chest CT imaging features for prediction of treatment response in cryptogenic and connective tissue disease-related organizing pneumonia. Eur Radiol 2020;30:2722-30. [PMID: 32040727 DOI: 10.1007/s00330-019-06651-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
383 Cho YJ, Kim WS, Choi YH, Ha JY, Lee S, Park SJ, Cheon JE, Kang HJ, Shin HY, Kim IO. Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules. PLoS One 2019;14:e0211969. [PMID: 30735557 DOI: 10.1371/journal.pone.0211969] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
384 Xiong J, Yu W, Ma J, Ren Y, Fu X, Zhao J. The Role of PET-Based Radiomic Features in Predicting Local Control of Esophageal Cancer Treated with Concurrent Chemoradiotherapy. Sci Rep 2018;8:9902. [PMID: 29967326 DOI: 10.1038/s41598-018-28243-x] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 2.8] [Reference Citation Analysis]
385 Attiyeh MA, Chakraborty J, McIntyre CA, Kappagantula R, Chou Y, Askan G, Seier K, Gonen M, Basturk O, Balachandran VP, Kingham TP, D'Angelica MI, Drebin JA, Jarnagin WR, Allen PJ, Iacobuzio-Donahue CA, Simpson AL, Do RK. CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2019;44:3148-57. [PMID: 31243486 DOI: 10.1007/s00261-019-02112-1] [Cited by in Crossref: 14] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
386 Sakai M, Nakano H, Kawahara D, Tanabe S, Takizawa T, Narita A, Yamada T, Sakai H, Ueda M, Sasamoto R, Kaidu M, Aoyama H, Ishikawa H, Utsunomiya S. Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy. Med Phys 2021;48:991-1002. [PMID: 33382467 DOI: 10.1002/mp.14699] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
387 Letzen B, Wang CJ, Chapiro J. The Role of Artificial Intelligence in Interventional Oncology: A Primer. Journal of Vascular and Interventional Radiology 2019;30:38-41.e1. [DOI: 10.1016/j.jvir.2018.08.032] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
388 Li H, Boimel P, Janopaul-Naylor J, Zhong H, Xiao Y, Ben-Josef E, Fan Y. Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer. Proc IEEE Int Symp Biomed Imaging. 2019;2019:846-849. [PMID: 31929858 DOI: 10.1109/isbi.2019.8759301] [Cited by in Crossref: 12] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
389 Zhao SS, Feng XL, Hu YC, Han Y, Tian Q, Sun YZ, Zhang J, Ge XW, Cheng SC, Li XL, Mao L, Shen SN, Yan LF, Cui GB, Wang W. Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images. BMC Neurol 2020;20:48. [PMID: 32033580 DOI: 10.1186/s12883-020-1613-y] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
390 Wang Y, Sun K, Liu Z, Chen G, Jia Y, Zhong S, Pan J, Huang L, Tian J. Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis. Cereb Cortex 2020;30:1117-28. [PMID: 31408101 DOI: 10.1093/cercor/bhz152] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 13.0] [Reference Citation Analysis]
391 Yu Y, Fan Y, Wang X, Zhu M, Hu M, Shi C, Hu C. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma. Eur Radiol 2021. [PMID: 34480625 DOI: 10.1007/s00330-021-08250-9] [Reference Citation Analysis]
392 Wu L, Wang C, Tan X, Cheng Z, Zhao K, Yan L, Liang Y, Liu Z, Liang C. Radiomics approach for preoperative identification of stages I-II and III-IV of esophageal cancer. Chin J Cancer Res. 2018;30:396-405. [PMID: 30210219 DOI: 10.21147/j.issn.1000-9604.2018.04.02] [Cited by in Crossref: 8] [Cited by in F6Publishing: 12] [Article Influence: 2.0] [Reference Citation Analysis]
393 Forghani R. Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors. Radiol Imaging Cancer 2020;2:e190047. [PMID: 33778721 DOI: 10.1148/rycan.2020190047] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
394 Liu B, Cheng J, Guo D, He X, Luo Y, Zeng Y, Li C. Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI. Clinical Radiology 2019;74:896.e1-8. [DOI: 10.1016/j.crad.2019.07.011] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
395 Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol 2020;65:24TR02. [PMID: 32688357 DOI: 10.1088/1361-6560/aba798] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
396 Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021;11:1523. [PMID: 34573865 DOI: 10.3390/diagnostics11091523] [Reference Citation Analysis]
397 Kim CH, Park H, Lee HY, Ahn JH, Lee SH, Sohn I, Choi JY, Kim HK. Computed Tomography Radiomics for Residual Positron Emission Tomography-Computed Tomography Uptake in Lymph Nodes after Treatment. Cancers (Basel) 2020;12:E3564. [PMID: 33260608 DOI: 10.3390/cancers12123564] [Reference Citation Analysis]
398 Wang H, Zhang J, Bao S, Liu J, Hou F, Huang Y, Chen H, Duan S, Hao D, Liu J. Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study. J Magn Reson Imaging 2020;52:873-82. [PMID: 32112598 DOI: 10.1002/jmri.27111] [Cited by in Crossref: 17] [Cited by in F6Publishing: 12] [Article Influence: 8.5] [Reference Citation Analysis]
399 Wang M, Liu H, Liu C, Li X, Jin C, Sun Q, Liu Z, Zheng J, Yang J. Prediction of adverse motor outcome for neonates with punctate white matter lesions by MRI images using radiomics strategy: protocol for a prospective cohort multicentre study. BMJ Open 2019;9:e023157. [PMID: 30948562 DOI: 10.1136/bmjopen-2018-023157] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
400 Al-Mallah MH. Radiomics in Hypertrophic Cardiomyopathy: The New Tool. JACC Cardiovasc Imaging 2019;12:1955-7. [PMID: 30878414 DOI: 10.1016/j.jcmg.2019.02.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
401 Dieckmeyer M, Inhuber S, Schlaeger S, Weidlich D, Mookiah MRK, Subburaj K, Burian E, Sollmann N, Kirschke JS, Karampinos DC, Baum T. Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength. Diagnostics (Basel) 2021;11:239. [PMID: 33557080 DOI: 10.3390/diagnostics11020239] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
402 Barucci A, Neri E. Adversarial radiomics: the rising of potential risks in medical imaging from adversarial learning. Eur J Nucl Med Mol Imaging 2020;47:2941-3. [DOI: 10.1007/s00259-020-04879-8] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
403 Pal A, Ali A, Young TR, Oostenbrink J, Prabhakar A, Prabhakar A, Deacon N, Arnold A, Eltayeb A, Yap C, Young DM, Tang A, Lakshmanan S, Lim YY, Pokarowski M, Kakodkar P. Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic. World J Radiol 2021; 13(9): 258-282 [PMID: 34630913 DOI: 10.4329/wjr.v13.i9.258] [Reference Citation Analysis]
404 Zhang BW, Zhang Y, Ye JD, Qiang JW. Use of relative CT values to evaluate the invasiveness of pulmonary subsolid nodules in patients with emphysema. Quant Imaging Med Surg 2021;11:204-14. [PMID: 33392022 DOI: 10.21037/qims-19-998] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
405 Zhang Q, Liao Y, Wang X, Zhang T, Feng J, Deng J, Shi K, Chen L, Feng L, Ma M, Xue L, Hou H, Dou X, Yu C, Ren L, Ding Y, Chen Y, Wu S, Chen Z, Zhang H, Zhuo C, Tian M. A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy. Eur J Nucl Med Mol Imaging 2021;48:2476-85. [PMID: 33420912 DOI: 10.1007/s00259-020-05108-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
406 Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 2016;1:207-26. [PMID: 28042608 DOI: 10.1080/23808993.2016.1164013] [Cited by in Crossref: 125] [Cited by in F6Publishing: 113] [Article Influence: 20.8] [Reference Citation Analysis]
407 Alis D, Bagcilar O, Senli YD, Isler C, Yergin M, Kocer N, Islak C, Kizilkilic O. The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas. Clin Radiol 2020;75:351-7. [PMID: 31973941 DOI: 10.1016/j.crad.2019.12.008] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
408 Li C, Yin J. Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients. Front Oncol 2021;11:671354. [PMID: 34041033 DOI: 10.3389/fonc.2021.671354] [Reference Citation Analysis]
409 Youk JH, Kwak JY, Lee E, Son EJ, Kim J. Grayscale Ultrasound Radiomic Features and Shear-Wave Elastography Radiomic Features in Benign and Malignant Breast Masses. Ultraschall Med 2020;41:390-6. [DOI: 10.1055/a-0917-6825] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
410 Porcu M, Solinas C, Mannelli L, Micheletti G, Lambertini M, Willard-Gallo K, Neri E, Flanders AE, Saba L. Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians. Crit Rev Oncol Hematol 2020;154:103068. [PMID: 32805498 DOI: 10.1016/j.critrevonc.2020.103068] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
411 Shi K, Xiao W, Wu G, Xiao Y, Lei Y, Yu J, Gu Y. Temporal-Spatial Feature Extraction of DSA Video and Its Application in AVM Diagnosis. Front Neurol 2021;12:655523. [PMID: 34122304 DOI: 10.3389/fneur.2021.655523] [Reference Citation Analysis]
412 Khawaja A, Bartholmai BJ, Rajagopalan S, Karwoski RA, Varghese C, Maldonado F, Peikert T. Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification. J Thorac Dis 2020;12:3303-16. [PMID: 32642254 DOI: 10.21037/jtd.2020.03.105] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
413 Du Q, Baine M, Bavitz K, McAllister J, Liang X, Yu H, Ryckman J, Yu L, Jiang H, Zhou S, Zhang C, Zheng D. Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction. PLoS One 2019;14:e0216480. [PMID: 31063500 DOI: 10.1371/journal.pone.0216480] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 6.7] [Reference Citation Analysis]
414 Moldovanu CG, Boca B, Lebovici A, Tamas-Szora A, Feier DS, Crisan N, Andras I, Buruian MM. Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features. J Pers Med 2020;11:8. [PMID: 33374569 DOI: 10.3390/jpm11010008] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
415 Wu J, Cao G, Sun X, Lee J, Rubin DL, Napel S, Kurian AW, Daniel BL, Li R. Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology. 2018;288:26-35. [PMID: 29714680 DOI: 10.1148/radiol.2018172462] [Cited by in Crossref: 48] [Cited by in F6Publishing: 45] [Article Influence: 12.0] [Reference Citation Analysis]
416 Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021;11:771787. [PMID: 34790581 DOI: 10.3389/fonc.2021.771787] [Reference Citation Analysis]
417 Huang Z, Cheng XQ, Liu HY, Bi XJ, Liu YN, Lv WZ, Xiong L, Deng YB. Relation of Carotid Plaque Features Detected with Ultrasonography-Based Radiomics to Clinical Symptoms. Transl Stroke Res 2021. [PMID: 34741749 DOI: 10.1007/s12975-021-00963-9] [Reference Citation Analysis]
418 Weng Q, Hui J, Wang H, Lan C, Huang J, Zhao C, Zheng L, Fang S, Chen M, Lu C, Bao Y, Pang P, Xu M, Mao W, Wang Z, Tu J, Huang Y, Ji J. Radiomic Feature-Based Nomogram: A Novel Technique to Predict EGFR-Activating Mutations for EGFR Tyrosin Kinase Inhibitor Therapy. Front Oncol 2021;11:590937. [PMID: 34422624 DOI: 10.3389/fonc.2021.590937] [Reference Citation Analysis]
419 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
420 Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, Fabro AT, Azevedo-Marques PM. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed 2018;159:23-30. [PMID: 29650315 DOI: 10.1016/j.cmpb.2018.02.015] [Cited by in Crossref: 54] [Cited by in F6Publishing: 48] [Article Influence: 13.5] [Reference Citation Analysis]
421 Feliciani G, Fioroni F, Grassi E, Bertolini M, Rosca A, Timon G, Galaverni M, Iotti C, Versari A, Iori M, Ciammella P. Radiomic Profiling of Head and Neck Cancer: 18F-FDG PET Texture Analysis as Predictor of Patient Survival. Contrast Media Mol Imaging 2018;2018:3574310. [PMID: 30363632 DOI: 10.1155/2018/3574310] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 3.5] [Reference Citation Analysis]
422 Huff DT, Ferjancic P, Namías M, Emamekhoo H, Perlman SB, Jeraj R. Image intensity histograms as imaging biomarkers: application to immune-related colitis. Biomed Phys Eng Express 2021;7. [PMID: 34534974 DOI: 10.1088/2057-1976/ac27c3] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
423 Orczyk C, Villers A, Rusinek H, Lepennec V, Bazille C, Giganti F, Mikheev A, Bernaudin M, Emberton M, Fohlen A, Valable S. Prostate cancer heterogeneity: texture analysis score based on multiple magnetic resonance imaging sequences for detection, stratification and selection of lesions at time of biopsy. BJU Int 2019;124:76-86. [PMID: 30378238 DOI: 10.1111/bju.14603] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
424 Alves AFF, Souza SA, Ruiz RL Jr, Reis TA, Ximenes AMG, Hasimoto EN, Lima RPS, Miranda JRA, Pina DR. Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients. Phys Eng Sci Med 2021;44:387-94. [PMID: 33730292 DOI: 10.1007/s13246-021-00988-2] [Reference Citation Analysis]
425 Smith HJ. The history of magnetic resonance imaging and its reflections in Acta Radiologica. Acta Radiol 2021;62:1481-98. [PMID: 34657480 DOI: 10.1177/02841851211050857] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
426 Stefano A, Comelli A, Bravatà V, Barone S, Daskalovski I, Savoca G, Sabini MG, Ippolito M, Russo G. A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method. BMC Bioinformatics 2020;21:325. [PMID: 32938360 DOI: 10.1186/s12859-020-03647-7] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
427 Smits M. MRI biomarkers in neuro-oncology. Nat Rev Neurol 2021;17:486-500. [PMID: 34149051 DOI: 10.1038/s41582-021-00510-y] [Reference Citation Analysis]
428 El Naqa I, Ten Haken RK. Can radiomics personalise immunotherapy? Lancet Oncol 2018;19:1138-9. [PMID: 30120042 DOI: 10.1016/S1470-2045(18)30429-7] [Cited by in Crossref: 11] [Cited by in F6Publishing: 4] [Article Influence: 2.8] [Reference Citation Analysis]
429 Spuhler KD, Ding J, Liu C, Sun J, Serrano-Sosa M, Moriarty M, Huang C. Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis. Magn Reson Med 2019;82:786-95. [PMID: 30957936 DOI: 10.1002/mrm.27758] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
430 Paul R, Schabath M, Balagurunathan Y, Liu Y, Li Q, Gillies R, Hall LO, Goldgof DB. Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features. Tomography 2019;5:192-200. [PMID: 30854457 DOI: 10.18383/j.tom.2018.00034] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
431 Tang A, Tam R, Cadrin-chênevert A, Guest W, Chong J, Barfett J, Chepelev L, Cairns R, Mitchell JR, Cicero MD, Poudrette MG, Jaremko JL, Reinhold C, Gallix B, Gray B, Geis R, O'connell T, Babyn P, Koff D, Ferguson D, Derkatch S, Bilbily A, Shabana W; for the Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Can Assoc Radiol J 2018;69:120-35. [DOI: 10.1016/j.carj.2018.02.002] [Cited by in Crossref: 147] [Cited by in F6Publishing: 113] [Article Influence: 36.8] [Reference Citation Analysis]
432 Hainc N, Stippich C, Reinhardt J, Stieltjes B, Blatow M, Mariani L, Bink A. Golden-angle radial sparse parallel (GRASP) MRI in clinical routine detection of pituitary microadenomas: First experience and feasibility. Magnetic Resonance Imaging 2019;60:38-43. [DOI: 10.1016/j.mri.2019.03.015] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
433 Bera K, Velcheti V, Madabhushi A. Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications. Am Soc Clin Oncol Educ Book 2018;38:1008-18. [PMID: 30231314 DOI: 10.1200/EDBK_199747] [Cited by in Crossref: 28] [Cited by in F6Publishing: 17] [Article Influence: 7.0] [Reference Citation Analysis]
434 Kasoji SK, Rivera JN, Gessner RC, Chang SX, Dayton PA. Early Assessment of Tumor Response to Radiation Therapy using High-Resolution Quantitative Microvascular Ultrasound Imaging. Theranostics 2018;8:156-68. [PMID: 29290799 DOI: 10.7150/thno.19703] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 4.5] [Reference Citation Analysis]
435 Moloney BM, McAnena PF, Elwahab SM, Fasoula A, Duchesne L, Gil Cano JD, Glynn C, O'Connell A, Ennis R, Lowery AJ, Kerin MJ. The Wavelia Microwave Breast Imaging system-tumour discriminating features and their clinical usefulness. Br J Radiol 2021;94:20210907. [PMID: 34581186 DOI: 10.1259/bjr.20210907] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
436 Cho HH, Kim CK, Park H. Overview of radiomics in prostate imaging and future directions. Br J Radiol 2021;:20210539. [PMID: 34797688 DOI: 10.1259/bjr.20210539] [Reference Citation Analysis]
437 Zheng Y, Chen L, Liu M, Wu J, Yu R, Lv F. Prediction of Clinical Outcome for High-Intensity Focused Ultrasound Ablation of Uterine Leiomyomas Using Multiparametric MRI Radiomics-Based Machine Leaning Model. Front Oncol 2021;11:618604. [PMID: 34567999 DOI: 10.3389/fonc.2021.618604] [Reference Citation Analysis]
438 Zhang N, Liang R, Gensheimer MF, Guo M, Zhu H, Yu J, Diehn M, Loo BW Jr, Li R, Wu J. Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. Theranostics 2020;10:11707-18. [PMID: 33052242 DOI: 10.7150/thno.50565] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
439 Lazzeroni M, Bunea H, Grosu AL, Baltas D, Toma-Dasu I, Dasu A. Mathematical Description of Changes in Tumour Oxygenation from Repeated Functional Imaging. Adv Exp Med Biol 2018;1072:195-200. [PMID: 30178345 DOI: 10.1007/978-3-319-91287-5_31] [Reference Citation Analysis]
440 You D, Kim MM, Aryal MP, Parmar H, Piert M, Lawrence TS, Cao Y. Tumor image signatures and habitats: a processing pipeline of multimodality metabolic and physiological images. J Med Imaging (Bellingham) 2018;5:011009. [PMID: 29181433 DOI: 10.1117/1.JMI.5.1.011009] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
441 Brink JA, Arenson RL, Grist TM, Lewin JS, Enzmann D. Bits and bytes: the future of radiology lies in informatics and information technology. Eur Radiol 2017;27:3647-51. [PMID: 28280932 DOI: 10.1007/s00330-016-4688-5] [Cited by in Crossref: 25] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
442 Tao W, Lu M, Zhou X, Montemezzi S, Bai G, Yue Y, Li X, Zhao L, Zhou C, Lu G. Machine Learning Based on Multi-Parametric MRI to Predict Risk of Breast Cancer. Front Oncol 2021;11:570747. [PMID: 33718131 DOI: 10.3389/fonc.2021.570747] [Reference Citation Analysis]
443 Ponsiglione A, Stanzione A, Cuocolo R, Ascione R, Gambardella M, De Giorgi M, Nappi C, Cuocolo A, Imbriaco M. Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol 2021. [PMID: 34812912 DOI: 10.1007/s00330-021-08375-x] [Reference Citation Analysis]
444 Qiu J, Peng S, Yin J, Wang J, Jiang J, Li Z, Song H, Zhang W. A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions. Interdiscip Sci 2021;13:61-72. [PMID: 33411162 DOI: 10.1007/s12539-020-00410-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
445 Khene ZE, Mathieu R, Peyronnet B, Kokorian R, Gasmi A, Khene F, Rioux-Leclercq N, Kammerer-Jacquet SF, Shariat S, Laguerre B, Bensalah K. Radiomics can predict tumour response in patients treated with Nivolumab for a metastatic renal cell carcinoma: an artificial intelligence concept. World J Urol 2020. [PMID: 32632555 DOI: 10.1007/s00345-020-03334-5] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
446 Akinci D'Antonoli T, Farchione A, Lenkowicz J, Chiappetta M, Cicchetti G, Martino A, Ottavianelli A, Manfredi R, Margaritora S, Bonomo L, Valentini V, Larici AR. CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk. Acad Radiol. 2020;27:497-507. [PMID: 31285150 DOI: 10.1016/j.acra.2019.05.019] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 5.3] [Reference Citation Analysis]
447 Han Y, Wang W, Yang Y, Sun YZ, Xiao G, Tian Q, Zhang J, Cui GB, Yan LF. Amide Proton Transfer Imaging in Predicting Isocitrate Dehydrogenase 1 Mutation Status of Grade II/III Gliomas Based on Support Vector Machine. Front Neurosci 2020;14:144. [PMID: 32153362 DOI: 10.3389/fnins.2020.00144] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
448 Dong M, Hou G, Li S, Li N, Zhang L, Xu K. Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging. Front Oncol 2020;10:558428. [PMID: 33489871 DOI: 10.3389/fonc.2020.558428] [Reference Citation Analysis]
449 Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, Ma ZL, Liu ZY. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016;34:2157-2164. [PMID: 27138577 DOI: 10.1200/jco.2015.65.9128] [Cited by in Crossref: 635] [Cited by in F6Publishing: 405] [Article Influence: 105.8] [Reference Citation Analysis]
450 Cho N. Breast Cancer Radiogenomics: Association of Enhancement Pattern at DCE MRI with Deregulation of mTOR Pathway. Radiology 2020;296:288-9. [PMID: 32478609 DOI: 10.1148/radiol.2020201607] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
451 Tagliafico AS, Valdora F, Mariscotti G, Durando M, Nori J, La Forgia D, Rosenberg I, Caumo F, Gandolfo N, Houssami N, Calabrese M. An exploratory radiomics analysis on digital breast tomosynthesis in women with mammographically negative dense breasts. The Breast 2018;40:92-6. [DOI: 10.1016/j.breast.2018.04.016] [Cited by in Crossref: 25] [Cited by in F6Publishing: 21] [Article Influence: 6.3] [Reference Citation Analysis]
452 Doda Khera R, Homayounieh F, Lades F, Schmidt B, Sedlmair M, Primak A, Saini S, Kalra MK. Can Dual-Energy Computed Tomography Quantitative Analysis and Radiomics Differentiate Normal Liver From Hepatic Steatosis and Cirrhosis?: . Journal of Computer Assisted Tomography 2020;44:223-9. [DOI: 10.1097/rct.0000000000000989] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
453 Li Z, Ma X, Shen F, Lu H, Xia Y, Lu J. Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models. BMC Med Imaging 2021;21:30. [PMID: 33593304 DOI: 10.1186/s12880-021-00560-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
454 Nagawa K, Suzuki M, Yamamoto Y, Inoue K, Kozawa E, Mimura T, Nakamura K, Nagata M, Niitsu M. Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies. Sci Rep 2021;11:9821. [PMID: 33972636 DOI: 10.1038/s41598-021-89311-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
455 Guerrisi A, Russillo M, Loi E, Ganeshan B, Ungania S, Desiderio F, Bruzzaniti V, Falcone I, Renna D, Ferraresi V, Caterino M, Solivetti FM, Cognetti F, Morrone A. Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach. Front Oncol 2021;11:704607. [PMID: 34692481 DOI: 10.3389/fonc.2021.704607] [Reference Citation Analysis]
456 Hall M. Artificial intelligence and nuclear medicine. Nucl Med Commun 2019;40:1-2. [PMID: 30362987 DOI: 10.1097/MNM.0000000000000937] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
457 Rabbani M, Kanevsky J, Kafi K, Chandelier F, Giles FJ. Role of artificial intelligence in the care of patients with nonsmall cell lung cancer. Eur J Clin Invest 2018;48:e12901. [DOI: 10.1111/eci.12901] [Cited by in Crossref: 29] [Cited by in F6Publishing: 18] [Article Influence: 7.3] [Reference Citation Analysis]
458 Wu J, Lian C, Ruan S, Mazur TR, Mutic S, Anastasio MA, Grigsby PW, Vera P, Li H. Treatment Outcome Prediction for Cancer Patients based on Radiomics and Belief Function Theory. IEEE Trans Radiat Plasma Med Sci 2019;3:216-24. [PMID: 31903444 DOI: 10.1109/TRPMS.2018.2872406] [Cited by in Crossref: 12] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
459 Wong AJ, Kanwar A, Mohamed AS, Fuller CD. Radiomics in head and neck cancer: from exploration to application. Transl Cancer Res 2016;5:371-82. [PMID: 30627523 DOI: 10.21037/tcr.2016.07.18] [Cited by in Crossref: 61] [Cited by in F6Publishing: 47] [Article Influence: 10.2] [Reference Citation Analysis]
460 Zhou Z, Maquilan GM, Thomas K, Wachsmann J, Wang J, Folkert MR, Albuquerque K. Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning. Technol Cancer Res Treat 2020;19:1533033820983804. [PMID: 33357081 DOI: 10.1177/1533033820983804] [Reference Citation Analysis]
461 Ren J, Qi M, Yuan Y, Tao X. Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the oral tongue and floor of mouth: a preliminary study. Acta Radiol 2021;62:453-61. [PMID: 32536260 DOI: 10.1177/0284185120931683] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
462 Streeter SS, Hunt B, Zuurbier RA, Wells WA, Paulsen KD, Pogue BW. Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures. Sci Rep 2021;11:21832. [PMID: 34750471 DOI: 10.1038/s41598-021-01414-z] [Reference Citation Analysis]
463 Feng X, Tustison NJ, Patel SH, Meyer CH. Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features. Front Comput Neurosci 2020;14:25. [PMID: 32322196 DOI: 10.3389/fncom.2020.00025] [Cited by in Crossref: 27] [Cited by in F6Publishing: 9] [Article Influence: 13.5] [Reference Citation Analysis]
464 Cova TFGG, Bento DJ, Nunes SCC. Computational Approaches in Theranostics: Mining and Predicting Cancer Data. Pharmaceutics 2019;11:E119. [PMID: 30871264 DOI: 10.3390/pharmaceutics11030119] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
465 Das IJ, McGee KP, Tyagi N, Wang H. Role and future of MRI in radiation oncology. Br J Radiol 2019;92:20180505. [PMID: 30383454 DOI: 10.1259/bjr.20180505] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 5.0] [Reference Citation Analysis]
466 Wesdorp NJ, van Goor VJ, Kemna R, Jansma EP, van Waesberghe JHTM, Swijnenburg RJ, Punt CJA, Huiskens J, Kazemier G. Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases: A systematic review of the literature. Surg Oncol 2021;38:101578. [PMID: 33866191 DOI: 10.1016/j.suronc.2021.101578] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
467 Kim KH, Kim J, Park H, Kim H, Lee SH, Sohn I, Lee HY, Park WY. Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non-small cell lung carcinoma patients. Thorac Cancer 2020;11:2542-51. [PMID: 32700470 DOI: 10.1111/1759-7714.13568] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
468 Papanikolaou N, Matos C, Koh DM. How to develop a meaningful radiomic signature for clinical use in oncologic patients. Cancer Imaging 2020;20:33. [PMID: 32357923 DOI: 10.1186/s40644-020-00311-4] [Cited by in Crossref: 17] [Cited by in F6Publishing: 13] [Article Influence: 8.5] [Reference Citation Analysis]
469 Li Y, Eresen A, Shangguan J, Yang J, Benson AB 3rd, Yaghmai V, Zhang Z. Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning. J Cancer Res Clin Oncol 2020;146:3165-74. [PMID: 32779023 DOI: 10.1007/s00432-020-03354-z] [Reference Citation Analysis]
470 Fu Y, Liu X, Yang Q, Sun J, Xie Y, Zhang Y, Zhang H. Radiomic features based on MRI for prediction of lymphovascular invasion in rectal cancer. Chin J Acad Radiol 2019;2:13-22. [DOI: 10.1007/s42058-019-00016-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
471 Prabhakar B, Singh RK, Yadav KS. Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device. Computerized Medical Imaging and Graphics 2021;87:101818. [DOI: 10.1016/j.compmedimag.2020.101818] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
472 Yang Y, Jin G, Pang Y, Wang W, Zhang H, Tuo G, Wu P, Wang Z, Zhu Z. The diagnostic accuracy of artificial intelligence in thoracic diseases: A protocol for systematic review and meta-analysis. Medicine (Baltimore) 2020;99:e19114. [PMID: 32049826 DOI: 10.1097/MD.0000000000019114] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
473 Li W, Yu K, Feng C, Zhao D. Molecular Subtypes Recognition of Breast Cancer in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Phenotypes from Radiomics Data. Comput Math Methods Med 2019;2019:6978650. [PMID: 31827586 DOI: 10.1155/2019/6978650] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
474 Shu J, Tang Y, Cui J, Yang R, Meng X, Cai Z, Zhang J, Xu W, Wen D, Yin H. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. Eur J Radiol 2018;109:8-12. [PMID: 30527316 DOI: 10.1016/j.ejrad.2018.10.005] [Cited by in Crossref: 50] [Cited by in F6Publishing: 45] [Article Influence: 12.5] [Reference Citation Analysis]
475 Mao X, Guo Y, Wen F, Liang H, Sun W, Lu Z. Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE). Cancer Imaging 2021;21:49. [PMID: 34384496 DOI: 10.1186/s40644-021-00418-2] [Reference Citation Analysis]
476 Dai Y, Yin P, Mao N, Sun C, Wu J, Cheng G, Hong N. Differentiation of Pelvic Osteosarcoma and Ewing Sarcoma Using Radiomic Analysis Based on T2-Weighted Images and Contrast-Enhanced T1-Weighted Images. Biomed Res Int 2020;2020:9078603. [PMID: 32462033 DOI: 10.1155/2020/9078603] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
477 Shu J, Wen D, Xi Y, Xia Y, Cai Z, Xu W, Meng X, Liu B, Yin H. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade. Eur J Radiol 2019;121:108738. [PMID: 31756634 DOI: 10.1016/j.ejrad.2019.108738] [Cited by in Crossref: 25] [Cited by in F6Publishing: 23] [Article Influence: 8.3] [Reference Citation Analysis]
478 Lapa C, Nestle U, Albert NL, Baues C, Beer A, Buck A, Budach V, Bütof R, Combs SE, Derlin T, Eiber M, Fendler WP, Furth C, Gani C, Gkika E, Grosu AL, Henkenberens C, Ilhan H, Löck S, Marnitz-Schulze S, Miederer M, Mix M, Nicolay NH, Niyazi M, Pöttgen C, Rödel CM, Schatka I, Schwarzenboeck SM, Todica AS, Weber W, Wegen S, Wiegel T, Zamboglou C, Zips D, Zöphel K, Zschaeck S, Thorwarth D, Troost EGC; “Arbeitsgemeinschaft Nuklearmedizin und Strahlentherapie der DEGRO und DGN”. Value of PET imaging for radiation therapy. Nuklearmedizin 2021. [PMID: 34261141 DOI: 10.1055/a-1525-7029] [Reference Citation Analysis]
479 Patel M, Zhan J, Natarajan K, Flintham R, Davies N, Sanghera P, Grist J, Duddalwar V, Peet A, Sawlani V. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma. Clin Radiol 2021;76:628.e17-27. [PMID: 33941364 DOI: 10.1016/j.crad.2021.03.019] [Reference Citation Analysis]
480 Ding H, Wu C, Liao N, Zhan Q, Sun W, Huang Y, Jiang Z, Li Y. Radiomics in Oncology: A 10-Year Bibliometric Analysis. Front Oncol 2021;11:689802. [PMID: 34616671 DOI: 10.3389/fonc.2021.689802] [Reference Citation Analysis]
481 Cook GJR, Goh V. A Role for FDG PET Radiomics in Personalized Medicine? Semin Nucl Med 2020;50:532-40. [PMID: 33059822 DOI: 10.1053/j.semnuclmed.2020.05.002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
482 Ma X, Zhang L, Huang D, Lyu J, Fang M, Hu J, Zang Y, Zhang D, Shao H, Ma L, Tian J, Dong D, Lou X. Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis. J Magn Reson Imaging 2019;49:1113-21. [PMID: 30408268 DOI: 10.1002/jmri.26287] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
483 Chen X, Wang H, Huang K, Liu H, Ding H, Zhang L, Zhang T, Yu W, He L. CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma. Front Oncol 2021;11:687884. [PMID: 34109133 DOI: 10.3389/fonc.2021.687884] [Reference Citation Analysis]
484 Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021;142:109882. [PMID: 34392105 DOI: 10.1016/j.ejrad.2021.109882] [Reference Citation Analysis]
485 Porcu M, Anzidei M, Suri JS, A Wasserman B, Anzalone N, Lucatelli P, Loi F, Montisci R, Sanfilippo R, Rafailidis V, Saba L. Carotid artery imaging: The study of intra-plaque vascularization and hemorrhage in the era of the "vulnerable" plaque. J Neuroradiol 2020;47:464-72. [PMID: 30954549 DOI: 10.1016/j.neurad.2019.03.009] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
486 Cho SY, Huff DT, Jeraj R, Albertini MR. FDG PET/CT for Assessment of Immune Therapy: Opportunities and Understanding Pitfalls. Semin Nucl Med 2020;50:518-31. [PMID: 33059821 DOI: 10.1053/j.semnuclmed.2020.06.001] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
487 Wang Q, Li C, Zhang J, Hu X, Fan Y, Ma K, Sparrelid E, Brismar TB. Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment. Cancers (Basel) 2021;13:5864. [PMID: 34831018 DOI: 10.3390/cancers13225864] [Reference Citation Analysis]
488 Zaidi H, Alavi A, Naqa IE. Novel Quantitative PET Techniques for Clinical Decision Support in Oncology. Semin Nucl Med 2018;48:548-64. [PMID: 30322481 DOI: 10.1053/j.semnuclmed.2018.07.003] [Cited by in Crossref: 20] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
489 Zhu C, Yu Y, Wang S, Wang X, Gao Y, Li C, Li J, Ge Y, Wu X. A Novel Clinical Radiomics Nomogram to Identify Crohn's Disease from Intestinal Tuberculosis. J Inflamm Res 2021;14:6511-21. [PMID: 34887674 DOI: 10.2147/JIR.S344563] [Reference Citation Analysis]
490 Peng Y, Zhou C, Lin P, Wen D, Wang X, Zhong X, Pan D, Que Q, Li X, Chen L, He Y, Yang H. Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma. Academic Radiology 2020;27:785-97. [DOI: 10.1016/j.acra.2019.07.029] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 6.5] [Reference Citation Analysis]
491 Liang M, Cai Z, Zhang H, Huang C, Meng Y, Zhao L, Li D, Ma X, Zhao X. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis. Acad Radiol. 2019;26:1495-1504. [PMID: 30711405 DOI: 10.1016/j.acra.2018.12.019] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 5.7] [Reference Citation Analysis]
492 Zhao L, Gong J, Xi Y, Xu M, Li C, Kang X, Yin Y, Qin W, Yin H, Shi M. MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma. Eur Radiol 2020;30:537-46. [PMID: 31372781 DOI: 10.1007/s00330-019-06211-x] [Cited by in Crossref: 26] [Cited by in F6Publishing: 22] [Article Influence: 8.7] [Reference Citation Analysis]
493 Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021. [PMID: 34309893 DOI: 10.1002/med.21846] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
494 Shi Z, Zhovannik I, Traverso A, Dankers FJWM, Deist TM, Kalendralis P, Monshouwer R, Bussink J, Fijten R, Aerts HJWL, Dekker A, Wee L. Distributed radiomics as a signature validation study using the Personal Health Train infrastructure. Sci Data 2019;6:218. [PMID: 31641134 DOI: 10.1038/s41597-019-0241-0] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 4.3] [Reference Citation Analysis]
495 Danti G, Flammia F, Matteuzzi B, Cozzi D, Berti V, Grazzini G, Pradella S, Recchia L, Brunese L, Miele V. Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging. Radiol Med 2021;126:1497-507. [PMID: 34427861 DOI: 10.1007/s11547-021-01408-x] [Reference Citation Analysis]
496 Lu L, Liang Y, Schwartz LH, Zhao B. Reliability of Radiomic Features Across Multiple Abdominal CT Image Acquisition Settings: A Pilot Study Using ACR CT Phantom. Tomography 2019;5:226-31. [PMID: 30854461 DOI: 10.18383/j.tom.2019.00005] [Cited by in Crossref: 8] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
497 Tian Q, Yan LF, Zhang X, Zhang X, Hu YC, Han Y, Liu ZC, Nan HY, Sun Q, Sun YZ, Yang Y, Yu Y, Zhang J, Hu B, Xiao G, Chen P, Tian S, Xu J, Wang W, Cui GB. Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging 2018;48:1518-28. [PMID: 29573085 DOI: 10.1002/jmri.26010] [Cited by in Crossref: 75] [Cited by in F6Publishing: 61] [Article Influence: 18.8] [Reference Citation Analysis]
498 Chen X, Fang M, Dong D, Wei X, Liu L, Xu X, Jiang X, Tian J, Liu Z. A Radiomics Signature in Preoperative Predicting Degree of Tumor Differentiation in Patients with Non-small Cell Lung Cancer. Acad Radiol 2018;25:1548-55. [PMID: 29572049 DOI: 10.1016/j.acra.2018.02.019] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 2.8] [Reference Citation Analysis]
499 Siviengphanom S, Gandomkar Z, Lewis SJ, Brennan PC. Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs. Acad Radiol 2021:S1076-6332(21)00468-2. [PMID: 34799256 DOI: 10.1016/j.acra.2021.09.025] [Reference Citation Analysis]
500 Plodkowski AJ, Araujo-Filho JAB, Simmers CDA, Girshman J, Raj M, Zheng J, Rimner A, Ginsberg MS. Pre-treatment CT imaging in stage IIIA lung cancer: Can we predict local recurrence after definitive chemoradiotherapy? Clin Imaging 2021;69:133-8. [PMID: 32721848 DOI: 10.1016/j.clinimag.2020.07.005] [Reference Citation Analysis]
501 Zhu H, Ai Y, Zhang J, Zhang J, Jin J, Xie C, Su H, Jin X. Preoperative Nomogram for Differentiation of Histological Subtypes in Ovarian Cancer Based on Computer Tomography Radiomics. Front Oncol 2021;11:642892. [PMID: 33842352 DOI: 10.3389/fonc.2021.642892] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
502 Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, Lian Z, Liu J, Luo X, Pei S, Mo X, Huang W, Liang C, Zhang B, Zhang S. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol. 2018;28:582-591. [PMID: 28828635 DOI: 10.1007/s00330-017-5005-7] [Cited by in Crossref: 90] [Cited by in F6Publishing: 91] [Article Influence: 18.0] [Reference Citation Analysis]
503 Pattira B. Editorial for "Radiomics Nomograms Based on Non-enhanced MRI and Clinical Risk Factors for the Differentiation of Chondrosarcoma from Enchondroma". J Magn Reson Imaging 2021;54:1324-5. [PMID: 33957006 DOI: 10.1002/jmri.27670] [Reference Citation Analysis]
504 Han Y, Chai F, Wei J, Yue Y, Cheng J, Gu D, Zhang Y, Tong T, Sheng W, Hong N, Ye Y, Wang Y, Tian J. Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis. Front Oncol 2020;10:1363. [PMID: 32923388 DOI: 10.3389/fonc.2020.01363] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
505 Sui H, Liu L, Li X, Zuo P, Cui J, Mo Z. CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions. J Thorac Dis 2019;11:1809-18. [PMID: 31285873 DOI: 10.21037/jtd.2019.05.32] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
506 Quan S, Chen H, Lin L, Shi Z, Ying H, Yuan C, Wang P, Liu S, Fan L. Automatic CT whole-lung segmentation in radiomics discrimination: Methodology and application in pneumonia diagnosis and distinguishment. Displays 2022;71:102144. [DOI: 10.1016/j.displa.2021.102144] [Reference Citation Analysis]
507 Jiang Z, Dong Y, Yang L, Lv Y, Dong S, Yuan S, Li D, Liu L. CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study. J Digit Imaging 2021. [PMID: 34327623 DOI: 10.1007/s10278-021-00484-9] [Reference Citation Analysis]
508 Shao S, Zheng N, Mao N, Xue X, Cui J, Gao P, Wang B. A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging. Clin Radiol 2021;76:472.e11-8. [PMID: 33752882 DOI: 10.1016/j.crad.2020.10.019] [Reference Citation Analysis]
509 Badve C, Kanekar S. Radiogenomics of Gliomas. Radiol Clin North Am 2021;59:441-55. [PMID: 33926688 DOI: 10.1016/j.rcl.2021.02.002] [Reference Citation Analysis]
510 McNitt-Gray M, Napel S, Jaggi A, Mattonen SA, Hadjiiski L, Muzi M, Goldgof D, Balagurunathan Y, Pierce LA, Kinahan PE, Jones EF, Nguyen A, Virkud A, Chan HP, Emaminejad N, Wahi-Anwar M, Daly M, Abdalah M, Yang H, Lu L, Lv W, Rahmim A, Gastounioti A, Pati S, Bakas S, Kontos D, Zhao B, Kalpathy-Cramer J, Farahani K. Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets. Tomography 2020;6:118-28. [PMID: 32548288 DOI: 10.18383/j.tom.2019.00031] [Cited by in Crossref: 12] [Cited by in F6Publishing: 17] [Article Influence: 12.0] [Reference Citation Analysis]
511 Rindi G, Wiedenmann B. Neuroendocrine neoplasia of the gastrointestinal tract revisited: towards precision medicine. Nat Rev Endocrinol 2020;16:590-607. [PMID: 32839579 DOI: 10.1038/s41574-020-0391-3] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
512 Boughdad S, Nioche C, Orlhac F, Jehl L, Champion L, Buvat I. Influence of age on radiomic features in 18F-FDG PET in normal breast tissue and in breast cancer tumors. Oncotarget 2018;9:30855-68. [PMID: 30112113 DOI: 10.18632/oncotarget.25762] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.8] [Reference Citation Analysis]
513 Ming X, Oei RW, Zhai R, Kong F, Du C, Hu C, Hu W, Zhang Z, Ying H, Wang J. MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma. Sci Rep. 2019;9:10412. [PMID: 31320729 DOI: 10.1038/s41598-019-46985-0] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 4.3] [Reference Citation Analysis]
514 Fu B, Qi S, Tao L, Xu H, Kang Y, Yao Y, Yang B, Duan Y, Chen H. Image Patch-Based Net Water Uptake and Radiomics Models Predict Malignant Cerebral Edema After Ischemic Stroke. Front Neurol 2020;11:609747. [PMID: 33424759 DOI: 10.3389/fneur.2020.609747] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
515 He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep 2016;6:34921. [PMID: 27721474 DOI: 10.1038/srep34921] [Cited by in Crossref: 94] [Cited by in F6Publishing: 100] [Article Influence: 15.7] [Reference Citation Analysis]
516 Murray JM, Kaissis G, Braren R, Kleesiek J. [A primer on radiomics]. Radiologe 2020;60:32-41. [PMID: 31820014 DOI: 10.1007/s00117-019-00617-w] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
517 Tagliafico AS. Imaging in multiple myeloma: Computed tomography or magnetic resonance imaging? World J Radiol 2021; 13(7): 223-226 [PMID: 34367508 DOI: 10.4329/wjr.v13.i7.223] [Reference Citation Analysis]
518 Kundu S, Chakraborty S, Chatterjee S, Das S, Achari RB, Mukhopadhyay J, Das PP, Mallick I, Arunsingh M, Bhattacharyyaa T, Ray S. De-Identification of Radiomics Data Retaining Longitudinal Temporal Information. J Med Syst 2020;44:99. [PMID: 32240368 DOI: 10.1007/s10916-020-01563-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
519 Gu H, Zhang X, di Russo P, Zhao X, Xu T. The Current State of Radiomics for Meningiomas: Promises and Challenges. Front Oncol 2020;10:567736. [PMID: 33194649 DOI: 10.3389/fonc.2020.567736] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
520 Huang X, Mai J, Huang Y, He L, Chen X, Wu X, Li Y, Yang X, Dong M, Huang J, Zhang F, Liang C, Liu Z. Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT. Clin Breast Cancer 2021;21:e388-401. [PMID: 33451965 DOI: 10.1016/j.clbc.2020.12.004] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
521 Borhani AA, Catania R, Velichko YS, Hectors S, Taouli B, Lewis S. Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response. Abdom Radiol (NY) 2021;46:3674-85. [PMID: 33891149 DOI: 10.1007/s00261-021-03085-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
522 Guerriero E, Ugga L, Cuocolo R. Artificial intelligence and pituitary adenomas: A review. Artif Intell Med Imaging 2020; 1(2): 70-77 [DOI: 10.35711/aimi.v1.i2.70] [Reference Citation Analysis]
523 Shen Y, Xu F, Zhu W, Hu H, Chen T, Li Q. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules. Ann Transl Med 2020;8:171. [PMID: 32309318 DOI: 10.21037/atm.2020.01.135] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
524 Cucchiara F, Del Re M, Valleggi S, Romei C, Petrini I, Lucchesi M, Crucitta S, Rofi E, De Liperi A, Chella A, Russo A, Danesi R. Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer. Front Oncol 2020;10:593831. [PMID: 33489892 DOI: 10.3389/fonc.2020.593831] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
525 Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54:890-901. [PMID: 34390014 DOI: 10.1111/apt.16563] [Reference Citation Analysis]
526 Fontaine P, Acosta O, Castelli J, De Crevoisier R, Müller H, Depeursinge A. The importance of feature aggregation in radiomics: a head and neck cancer study. Sci Rep 2020;10:19679. [PMID: 33184313 DOI: 10.1038/s41598-020-76310-z] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
527 Liu P, Wang H, Zheng S, Zhang F, Zhang X. Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging. Front Neurol 2020;11:248. [PMID: 32322236 DOI: 10.3389/fneur.2020.00248] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
528 Guan X, Wang S, Kuang P, Lu H, Zhang M, Qian D, Xu X. The Usefulness of Imaging Quantification in Discriminating Non-Calcified Pulmonary Hamartoma From Adenocarcinoma. Front Oncol 2020;10:568069. [PMID: 33194653 DOI: 10.3389/fonc.2020.568069] [Reference Citation Analysis]
529 Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel) 2019;11:E1673. [PMID: 31661863 DOI: 10.3390/cancers11111673] [Cited by in Crossref: 33] [Cited by in F6Publishing: 30] [Article Influence: 11.0] [Reference Citation Analysis]
530 Liu Y, Zhang X, Feng N, Yin L, He Y, Xu X, Lu H. The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis. Acta Radiol 2018;59:1239-46. [PMID: 29430935 DOI: 10.1177/0284185118756951] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 4.8] [Reference Citation Analysis]
531 Hochhegger B, Zanon M, Altmayer S, Pacini GS, Balbinot F, Francisco MZ, Dalla Costa R, Watte G, Santos MK, Barros MC, Penha D, Irion K, Marchiori E. Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review. Lung 2018;196:633-42. [DOI: 10.1007/s00408-018-0156-0] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
532 Phillips I, Ezhil V, Hussein M, South C, Nisbet A, Alobaidli S, Prakash V, Ajaz M, Wang H, Evans P. Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan. BJR Open 2019;1:20180001. [PMID: 33178905 DOI: 10.1259/bjro.20180001] [Reference Citation Analysis]
533 Liu S, Liu S, Zhang C, Yu H, Liu X, Hu Y, Xu W, Tang X, Fu Q. Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-small-Cell Lung Cancer. Front Oncol 2020;10:1268. [PMID: 33014770 DOI: 10.3389/fonc.2020.01268] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
534 de Jesus FM, Yin Y, Mantzorou-Kyriaki E, Kahle XU, de Haas RJ, Yakar D, Glaudemans AWJM, Noordzij W, Kwee TC, Nijland M. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features. Eur J Nucl Med Mol Imaging 2021. [PMID: 34850248 DOI: 10.1007/s00259-021-05626-3] [Reference Citation Analysis]
535 Jin X, Ai Y, Zhang J, Zhu H, Jin J, Teng Y, Chen B, Xie C. Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images. Eur Radiol 2020;30:4117-24. [PMID: 32078013 DOI: 10.1007/s00330-020-06692-1] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
536 Taghavi M, Staal FC, Simões R, Hong EK, Lambregts DM, van der Heide UA, Beets-Tan RG, Maas M. CT radiomics models are unable to predict new liver metastasis after successful thermal ablation of colorectal liver metastases. Acta Radiol 2021;:2841851211060437. [PMID: 34918955 DOI: 10.1177/02841851211060437] [Reference Citation Analysis]
537 Shin I, Kim YJ, Han K, Lee E, Kim HJ, Shin JH, Moon HJ, Youk JH, Kim KG, Kwak JY. Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland. Ultrasonography 2020;39:257-65. [PMID: 32299197 DOI: 10.14366/usg.19069] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
538 Haider SP, Burtness B, Yarbrough WG, Payabvash S. Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas. Cancers Head Neck 2020;5:6. [PMID: 32391171 DOI: 10.1186/s41199-020-00053-7] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
539 Vaidyanathan A, van der Lubbe MFJA, Leijenaar RTH, van Hoof M, Zerka F, Miraglio B, Primakov S, Postma AA, Bruintjes TD, Bilderbeek MAL, Sebastiaan H, Dammeijer PFM, van Rompaey V, Woodruff HC, Vos W, Walsh S, van de Berg R, Lambin P. Deep learning for the fully automated segmentation of the inner ear on MRI. Sci Rep 2021;11:2885. [PMID: 33536451 DOI: 10.1038/s41598-021-82289-y] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
540 Qiao X, Li Z, Li L, Ji C, Li H, Shi T, Gu Q, Liu S, Zhou Z, Zhou K. Preoperative T2-weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages. Abdom Radiol (NY) 2021;46:1487-97. [PMID: 33047226 DOI: 10.1007/s00261-020-02802-1] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
541 Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 2018;28:4514-23. [PMID: 29761357 DOI: 10.1007/s00330-018-5463-6] [Cited by in Crossref: 46] [Cited by in F6Publishing: 45] [Article Influence: 11.5] [Reference Citation Analysis]
542 Moura LVD, Mattjie C, Dartora CM, Barros RC, Marques da Silva AM. Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography. Front Digit Health 2022;3:662343. [DOI: 10.3389/fdgth.2021.662343] [Reference Citation Analysis]
543 Xu Y, Ji W, Hou L, Lin S, Shi Y, Zhou C, Meng Y, Wang W, Chen X, Wang M, Yang H. Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma. Front Oncol 2021;11:704994. [PMID: 34513686 DOI: 10.3389/fonc.2021.704994] [Reference Citation Analysis]
544 Song Y, Li J, Wang H, Liu B, Yuan C, Liu H, Zheng Z, Min F, Li Y. Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study. Acad Radiol 2021:S1076-6332(21)00220-8. [PMID: 34238656 DOI: 10.1016/j.acra.2021.05.005] [Reference Citation Analysis]
545 Lee J, Li B, Cui Y, Sun X, Wu J, Zhu H, Yu J, Gensheimer MF, Loo BW, Diehn M, Li R. A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer. International Journal of Radiation Oncology*Biology*Physics 2018;102:1098-106. [DOI: 10.1016/j.ijrobp.2018.01.006] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 2.5] [Reference Citation Analysis]
546 Zheng H, Miao Q, Liu Y, Raman SS, Scalzo F, Sung K. Integrative Machine Learning Prediction of Prostate Biopsy Results From Negative Multiparametric MRI. J Magn Reson Imaging 2021. [PMID: 34160114 DOI: 10.1002/jmri.27793] [Reference Citation Analysis]
547 Prior F, Almeida J, Kathiravelu P, Kurc T, Smith K, Fitzgerald TJ, Saltz J. Open access image repositories: high-quality data to enable machine learning research. Clin Radiol 2020;75:7-12. [PMID: 31040006 DOI: 10.1016/j.crad.2019.04.002] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
548 Chen Y, Xia Y, Tolat PP, Long L, Jiang Z, Huang Z, Tang Q. Comparison of Conventional Gadoxetate Disodium-Enhanced MRI Features and Radiomics Signatures With Machine Learning for Diagnosing Microvascular Invasion. AJR Am J Roentgenol 2021;216:1510-20. [PMID: 33826360 DOI: 10.2214/AJR.20.23255] [Reference Citation Analysis]
549 Shen C, Liu Z, Wang Z, Guo J, Zhang H, Wang Y, Qin J, Li H, Fang M, Tang Z, Li Y, Qu J, Tian J. Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction. Transl Oncol 2018;11:815-24. [PMID: 29727831 DOI: 10.1016/j.tranon.2018.04.005] [Cited by in Crossref: 32] [Cited by in F6Publishing: 36] [Article Influence: 8.0] [Reference Citation Analysis]
550 Hua W, Xiao T, Jiang X, Liu Z, Wang M, Zheng H, Wang S. Lymph-vascular space invasion prediction in cervical cancer: Exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI. Biomedical Signal Processing and Control 2020;58:101869. [DOI: 10.1016/j.bspc.2020.101869] [Cited by in Crossref: 14] [Cited by in F6Publishing: 3] [Article Influence: 7.0] [Reference Citation Analysis]
551 Hussain MA, Hamarneh G, Garbi R. Noninvasive Determination of Gene Mutations in Clear Cell Renal Cell Carcinoma Using Multiple Instance Decisions Aggregated CNN. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-lópez C, Fichtinger G, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018. pp. 657-65. [DOI: 10.1007/978-3-030-00934-2_73] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
552 Drukker K, Giger ML, Joe BN, Kerlikowske K, Greenwood H, Drukteinis JS, Niell B, Fan B, Malkov S, Avila J, Kazemi L, Shepherd J. Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set. Radiology 2019;290:621-8. [PMID: 30526359 DOI: 10.1148/radiol.2018180608] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
553 Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A, Bettinardi V, Partelli S, Gianolli L, Falconi M, Picchio M. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 2021. [PMID: 33835220 DOI: 10.1007/s00259-021-05338-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
554 Roy S, Whitehead TD, Li S, Ademuyiwa FO, Wahl RL, Dehdashti F, Shoghi KI. Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer. Eur J Nucl Med Mol Imaging 2021. [PMID: 34328530 DOI: 10.1007/s00259-021-05489-8] [Reference Citation Analysis]
555 Rizzo S, Manganaro L, Dolciami M, Gasparri ML, Papadia A, Del Grande F. Computed Tomography Based Radiomics as a Predictor of Survival in Ovarian Cancer Patients: A Systematic Review. Cancers (Basel) 2021;13:573. [PMID: 33540655 DOI: 10.3390/cancers13030573] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
556 Bologna M, Calareso G, Resteghini C, Sdao S, Montin E, Corino V, Mainardi L, Licitra L, Bossi P. Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer. NMR Biomed 2020;:e4265. [PMID: 32009265 DOI: 10.1002/nbm.4265] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
557 Patyk M, Silicki J, Mazur R, Kręcichwost R, Sokołowska-Dąbek D, Zaleska-Dorobisz U. Radiomics - the value of the numbers in present and future radiology. Pol J Radiol 2018;83:e171-4. [PMID: 30627231 DOI: 10.5114/pjr.2018.75794] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
558 Kim D, Wang N, Ravikumar V, Raghuram DR, Li J, Patel A, Wendt RE 3rd, Rao G, Rao A. Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Pre-operative Multiparametric Magnetic Resonance Imaging. Front Comput Neurosci 2019;13:52. [PMID: 31417387 DOI: 10.3389/fncom.2019.00052] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 3.3] [Reference Citation Analysis]
559 Zhang H, Yin F, Chen M, Qi A, Yang L, Cui W, Yang S, Wen G. [Predicting postoperative recurrence of stage Ⅰ-Ⅲ renal clear cell carcinoma based on preoperative CT radiomics feature nomogram]. Nan Fang Yi Ke Da Xue Xue Bao 2021;41:1358-65. [PMID: 34658350 DOI: 10.12122/j.issn.1673-4254.2021.09.10] [Reference Citation Analysis]
560 Zhovannik I, Bussink J, Traverso A, Shi Z, Kalendralis P, Wee L, Dekker A, Fijten R, Monshouwer R. Learning from scanners: Bias reduction and feature correction in radiomics. Clin Transl Radiat Oncol 2019;19:33-8. [PMID: 31417963 DOI: 10.1016/j.ctro.2019.07.003] [Cited by in Crossref: 22] [Cited by in F6Publishing: 21] [Article Influence: 7.3] [Reference Citation Analysis]
561 Cao Y, Zhong X, Diao W, Mu J, Cheng Y, Jia Z. Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations. Cancers (Basel) 2021;13:2436. [PMID: 34069887 DOI: 10.3390/cancers13102436] [Reference Citation Analysis]
562 McCoy DB, Dupont SM, Gros C, Cohen-Adad J, Huie RJ, Ferguson A, Duong-Fernandez X, Thomas LH, Singh V, Narvid J, Pascual L, Kyritsis N, Beattie MS, Bresnahan JC, Dhall S, Whetstone W, Talbott JF; TRACK-SCI Investigators. Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury. AJNR Am J Neuroradiol 2019;40:737-44. [PMID: 30923086 DOI: 10.3174/ajnr.A6020] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 3.7] [Reference Citation Analysis]
563 Lin M, Wynne JF, Zhou B, Wang T, Lei Y, Curran WJ, Liu T, Yang X. Artificial intelligence in tumor subregion analysis based on medical imaging: A review. J Appl Clin Med Phys 2021;22:10-26. [PMID: 34164913 DOI: 10.1002/acm2.13321] [Reference Citation Analysis]
564 Zhu YC, Jin PF, Bao J, Jiang Q, Wang X. Thyroid ultrasound image classification using a convolutional neural network. Ann Transl Med 2021;9:1526. [PMID: 34790732 DOI: 10.21037/atm-21-4328] [Reference Citation Analysis]
565 Mao N, Dai Y, Lin F, Ma H, Duan S, Xie H, Zhao W, Hong N. Radiomics Nomogram of DCE-MRI for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer. Front Oncol 2020;10:541849. [PMID: 33381444 DOI: 10.3389/fonc.2020.541849] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
566 Cetin I, Raisi-Estabragh Z, Petersen SE, Napel S, Piechnik SK, Neubauer S, Gonzalez Ballester MA, Camara O, Lekadir K. Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank. Front Cardiovasc Med 2020;7:591368. [PMID: 33240940 DOI: 10.3389/fcvm.2020.591368] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
567 Song C, Wang M, Luo Y, Chen J, Peng Z, Wang Y, Zhang H, Li ZP, Shen J, Huang B, Feng ST. Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images. Ann Transl Med 2021;9:833. [PMID: 34164467 DOI: 10.21037/atm-21-25] [Reference Citation Analysis]
568 Zhu D, Zhang M, Li Q, Liu J, Zhuang Y, Chen Q, Chen C, Xiang Y, Zhang Y, Yang Y. Can perihaematomal radiomics features predict haematoma expansion? Clin Radiol 2021;76:629.e1-9. [PMID: 33858695 DOI: 10.1016/j.crad.2021.03.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
569 Tipaldi MA, Ronconi E, Lucertini E, Krokidis M, Zerunian M, Polidori T, Begini P, Marignani M, Mazzuca F, Caruso D, Rossi M, Laghi A. Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features. Diagnostics (Basel) 2021;11:956. [PMID: 34073545 DOI: 10.3390/diagnostics11060956] [Reference Citation Analysis]
570 Zhu X, Dong D, Chen Z, Fang M, Zhang L, Song J, Yu D, Zang Y, Liu Z, Shi J, Tian J. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 2018;28:2772-8. [PMID: 29450713 DOI: 10.1007/s00330-017-5221-1] [Cited by in Crossref: 78] [Cited by in F6Publishing: 73] [Article Influence: 19.5] [Reference Citation Analysis]
571 Ahmed AA, Elmohr MM, Fuentes D, Habra MA, Fisher SB, Perrier ND, Zhang M, Elsayes KM. Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma. Clin Radiol 2020;75:479.e17-22. [PMID: 32089260 DOI: 10.1016/j.crad.2020.01.012] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
572 Sun Z, Hu S, Ge Y, Wang J, Duan S, Song J, Hu C, Li Y. Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features. J Xray Sci Technol 2020;28:449-59. [PMID: 32176676 DOI: 10.3233/XST-200642] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
573 Yu XY, Ren J, Jia Y, Wu H, Niu G, Liu A, Gao Y, Hao F, Xie L. Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer. Front Oncol 2021;11:765652. [PMID: 34790579 DOI: 10.3389/fonc.2021.765652] [Reference Citation Analysis]
574 Meng F, Guo Y, Li M, Lu X, Wang S, Zhang L, Zhang H. Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules. Transl Oncol 2021;14:100936. [PMID: 33221688 DOI: 10.1016/j.tranon.2020.100936] [Reference Citation Analysis]
575 Zheng Z, Wang B, Zhao Q, Zhang Y, Wei J, Meng L, Xin Y, Jiang X. Research progress on mechanism and imaging of temporal lobe injury induced by radiotherapy for head and neck cancer. Eur Radiol 2021. [PMID: 34327577 DOI: 10.1007/s00330-021-08164-6] [Reference Citation Analysis]
576 Xu W, Wu W, Zheng Y, Chen Z, Tao X, Zhang D, Zhao J, Wang K, Guo B, Luo Q, Han Q, Zhou Y, Ye S. A Computed Tomography Radiomics-Based Prediction Model on Interstitial Lung Disease in Anti-MDA5-Positive Dermatomyositis. Front Med (Lausanne) 2021;8:768052. [PMID: 34912828 DOI: 10.3389/fmed.2021.768052] [Reference Citation Analysis]
577 Park BW, Kim JK, Heo C, Park KJ. Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters.Sci Rep. 2020;10:3852. [PMID: 32123281 DOI: 10.1038/s41598-020-60868-9] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
578 Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90:20160665. [PMID: 27936886 DOI: 10.1259/bjr.20160665] [Cited by in Crossref: 173] [Cited by in F6Publishing: 160] [Article Influence: 28.8] [Reference Citation Analysis]
579 Xia W, Hu B, Li H, Shi W, Tang Y, Yu Y, Geng C, Wu Q, Yang L, Yu Z, Geng D, Li Y. Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model. J Magn Reson Imaging 2021;54:880-7. [PMID: 33694250 DOI: 10.1002/jmri.27592] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
580 Zhu DQ, Chen Q, Xiang YL, Zhan CY, Zhang MY, Chen C, Zhuge QC, Chen WJ, Yang XM, Yang YJ. Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model. Aging (Albany NY) 2021;13:12833-48. [PMID: 33946042 DOI: 10.18632/aging.202954] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
581 Ibrahim A, Refaee T, Primakov S, Barufaldi B, Acciavatti RJ, Granzier RWY, Hustinx R, Mottaghy FM, Woodruff HC, Wildberger JE, Lambin P, Maidment ADA. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization. Cancers (Basel) 2021;13:1848. [PMID: 33924382 DOI: 10.3390/cancers13081848] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
582 Tong X, Feng X, Peng F, Niu H, Zhang B, Yuan F, Jin W, Wu Z, Zhao Y, Liu A, Wang D. Morphology-based radiomics signature: a novel determinant to identify multiple intracranial aneurysms rupture. Aging (Albany NY) 2021;13:13195-210. [PMID: 33971625 DOI: 10.18632/aging.203001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
583 Lohmann P, Kocher M, Ruge MI, Visser-Vandewalle V, Shah NJ, Fink GR, Langen KJ, Galldiks N. PET/MRI Radiomics in Patients With Brain Metastases. Front Neurol 2020;11:1. [PMID: 32116995 DOI: 10.3389/fneur.2020.00001] [Cited by in Crossref: 19] [Cited by in F6Publishing: 11] [Article Influence: 9.5] [Reference Citation Analysis]
584 Dolley S. Big Data's Role in Precision Public Health. Front Public Health 2018;6:68. [PMID: 29594091 DOI: 10.3389/fpubh.2018.00068] [Cited by in Crossref: 65] [Cited by in F6Publishing: 42] [Article Influence: 16.3] [Reference Citation Analysis]
585 Geng Z, Zhang Y, Wang S, Li H, Zhang C, Yin S, Xie C, Dai Y. Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features. Magn Reson Med Sci 2021;20:253-63. [PMID: 32788505 DOI: 10.2463/mrms.mp.2020-0060] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
586 De Robertis R, Maris B, Cardobi N, Tinazzi Martini P, Gobbo S, Capelli P, Ortolani S, Cingarlini S, Paiella S, Landoni L, Butturini G, Regi P, Scarpa A, Tortora G, D'Onofrio M. Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol 2018;28:2582-91. [PMID: 29352378 DOI: 10.1007/s00330-017-5236-7] [Cited by in Crossref: 30] [Cited by in F6Publishing: 28] [Article Influence: 7.5] [Reference Citation Analysis]
587 Pérez-Beteta J, Martínez-González A, Pérez-García VM. A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours. J R Soc Interface 2018;15:20180503. [PMID: 30958226 DOI: 10.1098/rsif.2018.0503] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
588 [DOI: 10.1101/2020.02.29.20029603] [Cited by in Crossref: 48] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
589 Sun L, Zhang S, Chen H, Luo L. Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning. Front Neurosci 2019;13:810. [PMID: 31474816 DOI: 10.3389/fnins.2019.00810] [Cited by in Crossref: 36] [Cited by in F6Publishing: 12] [Article Influence: 12.0] [Reference Citation Analysis]
590 Li S, Luo T, Ding C, Huang Q, Guan Z, Zhang H. Detailed identification of epidermal growth factor receptor mutations in lung adenocarcinoma: Combining radiomics with machine learning. Med Phys 2020;47:3458-66. [DOI: 10.1002/mp.14238] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
591 Zhao Y, Liu G, Sun Q, Zhai G, Wu G, Li ZC. Validation of CT radiomics for prediction of distant metastasis after surgical resection in patients with clear cell renal cell carcinoma: exploring the underlying signaling pathways. Eur Radiol 2021;31:5032-40. [PMID: 33439312 DOI: 10.1007/s00330-020-07590-2] [Reference Citation Analysis]
592 Orlhac F, Nioche C, Soussan M, Buvat I. Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data. J Nucl Med 2017;58:387-92. [PMID: 27754906 DOI: 10.2967/jnumed.116.181859] [Cited by in Crossref: 63] [Cited by in F6Publishing: 58] [Article Influence: 10.5] [Reference Citation Analysis]
593 Bak SH, Park H, Sohn I, Lee SH, Ahn MJ, Lee HY. Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer. Sci Rep 2019;9:8730. [PMID: 31217441 DOI: 10.1038/s41598-019-45117-y] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
594 Napel S, Mu W, Jardim-Perassi BV, Aerts HJWL, Gillies RJ. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer 2018;124:4633-49. [PMID: 30383900 DOI: 10.1002/cncr.31630] [Cited by in Crossref: 48] [Cited by in F6Publishing: 42] [Article Influence: 12.0] [Reference Citation Analysis]
595 Luo S, Zhou ZM, Guo DJ, Li CM, Liu H, Wu XJ, Liang S, Zhao XY, Chen T, Sun D, Shi XL, Zhong WJ, Zhang W. Radiomics-based classification models for HBV-related cirrhotic patients with covert hepatic encephalopathy. Brain Behav 2021;11:e01970. [PMID: 33236529 DOI: 10.1002/brb3.1970] [Reference Citation Analysis]
596 Kalisvaart GM, Bloem JL, Bovée JVMG, van de Sande MAJ, Gelderblom H, van der Hage JA, Hartgrink HH, Krol ADG, de Geus-Oei LF, Grootjans W. Personalising sarcoma care using quantitative multimodality imaging for response assessment. Clin Radiol 2021;76:313.e1-313.e13. [PMID: 33483087 DOI: 10.1016/j.crad.2020.12.009] [Reference Citation Analysis]
597 Shan QY, Hu HT, Feng ST, Peng ZP, Chen SL, Zhou Q, Li X, Xie XY, Lu MD, Wang W, Kuang M. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging. 2019;19:11. [PMID: 30813956 DOI: 10.1186/s40644-019-0197-5] [Cited by in Crossref: 44] [Cited by in F6Publishing: 40] [Article Influence: 14.7] [Reference Citation Analysis]
598 Liao H, Xiong T, Peng J, Xu L, Liao M, Zhang Z, Wu Z, Yuan K, Zeng Y. Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning. Ann Surg Oncol. 2020;27:2359-2369. [PMID: 31916093 DOI: 10.1245/s10434-019-08190-1] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
599 Su CQ, Chen XT, Duan SF, Zhang JX, You YP, Lu SS, Hong XN. A radiomics-based model to differentiate glioblastoma from solitary brain metastases. Clin Radiol 2021;76:629.e11-8. [PMID: 34092362 DOI: 10.1016/j.crad.2021.04.012] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
600 Xu M, Qi S, Yue Y, Teng Y, Xu L, Yao Y, Qian W. Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset. Biomed Eng Online 2019;18:2. [PMID: 30602393 DOI: 10.1186/s12938-018-0619-9] [Cited by in Crossref: 33] [Cited by in F6Publishing: 14] [Article Influence: 11.0] [Reference Citation Analysis]
601 Ortiz-Ramón R, Valdés Hernández MDC, González-Castro V, Makin S, Armitage PA, Aribisala BS, Bastin ME, Deary IJ, Wardlaw JM, Moratal D. Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images. Comput Med Imaging Graph 2019;74:12-24. [PMID: 30921550 DOI: 10.1016/j.compmedimag.2019.02.006] [Cited by in Crossref: 14] [Cited by in F6Publishing: 11] [Article Influence: 4.7] [Reference Citation Analysis]
602 Spuhler KD, Teruel JR, Galavis PE. Assessing the reproducibility of CBCT-derived radiomics features using a novel three-dimensional printed phantom. Med Phys 2021. [PMID: 34120354 DOI: 10.1002/mp.15043] [Reference Citation Analysis]
603 Desideri I, Loi M, Francolini G, Becherini C, Livi L, Bonomo P. Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art. Front Oncol 2020;10:1708. [PMID: 33117669 DOI: 10.3389/fonc.2020.01708] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
604 Wang X, Mao K, Wang L, Yang P, Lu D, He P. An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images. Sensors (Basel) 2019;19:E194. [PMID: 30621101 DOI: 10.3390/s19010194] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 4.3] [Reference Citation Analysis]
605 Bashir U, Kawa B, Siddique M, Mak SM, Nair A, Mclean E, Bille A, Goh V, Cook G. Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features. Br J Radiol 2019;92:20190159. [PMID: 31166787 DOI: 10.1259/bjr.20190159] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
606 Jiang M, Zhang Y, Xu J, Ji M, Guo Y, Guo Y, Xiao J, Yao X, Shi H, Zeng M. Assessing EGFR gene mutation status in non-small cell lung cancer with imaging features from PET/CT. Nucl Med Commun 2019;40:842-9. [PMID: 31290849 DOI: 10.1097/MNM.0000000000001043] [Cited by in Crossref: 6] [Cited by in F6Publishing: 9] [Article Influence: 2.0] [Reference Citation Analysis]
607 Frøkjær JB, Lisitskaya MV, Jørgensen AS, Østergaard LR, Hansen TM, Drewes AM, Olesen SS. Pancreatic magnetic resonance imaging texture analysis in chronic pancreatitis: a feasibility and validation study. Abdom Radiol 2020;45:1497-506. [DOI: 10.1007/s00261-020-02512-8] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
608 Aiello M, Cavaliere C, D'Albore A, Salvatore M. The Challenges of Diagnostic Imaging in the Era of Big Data. J Clin Med 2019;8:E316. [PMID: 30845692 DOI: 10.3390/jcm8030316] [Cited by in Crossref: 21] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]
609 Ahn HK, Lee H, Kim SG, Hyun SH. Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. Clin Radiol 2019;74:467-73. [PMID: 30898382 DOI: 10.1016/j.crad.2019.02.008] [Cited by in Crossref: 23] [Cited by in F6Publishing: 19] [Article Influence: 7.7] [Reference Citation Analysis]
610 Gharavi SMH, Faghihimehr A. Clinical Application of Artificial Intelligence in PET Imaging of Head and Neck Cancer. PET Clin 2022;17:65-76. [PMID: 34809871 DOI: 10.1016/j.cpet.2021.09.004] [Reference Citation Analysis]
611 Sun R, Orlhac F, Robert C, Reuzé S, Schernberg A, Buvat I, Deutsch E, Ferté C. In Regard to Mattonen et al. International Journal of Radiation Oncology*Biology*Physics 2016;95:1544-5. [DOI: 10.1016/j.ijrobp.2016.03.038] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 1.8] [Reference Citation Analysis]
612 Mazzaschi G, Milanese G, Pagano P, Madeddu D, Gnetti L, Trentini F, Falco A, Frati C, Lorusso B, Lagrasta C, Minari R, Ampollini L, Silva M, Sverzellati N, Quaini F, Roti G, Tiseo M. Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC. Lung Cancer 2020;144:30-9. [PMID: 32361033 DOI: 10.1016/j.lungcan.2020.04.006] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
613 Kaur A, Singh Y, Neeru N, Kaur L, Singh A. A Survey on Deep Learning Approaches to Medical Images and a Systematic Look up into Real-Time Object Detection. Arch Computat Methods Eng. [DOI: 10.1007/s11831-021-09649-9] [Reference Citation Analysis]
614 Wang L, Li T, Hong J, Zhang M, Ouyang M, Zheng X, Tang K. 18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma. Quant Imaging Med Surg 2021;11:215-25. [PMID: 33392023 DOI: 10.21037/qims-20-337] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
615 Farchione A, Larici AR, Masciocchi C, Cicchetti G, Congedo MT, Franchi P, Gatta R, Lo Cicero S, Valentini V, Bonomo L, Manfredi R. Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis-usefulness of density correction of volumetric CT data. Radiol Med 2020;125:625-35. [PMID: 32125637 DOI: 10.1007/s11547-020-01157-3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
616 Richardson ML, Garwood ER, Lee Y, Li MD, Lo HS, Nagaraju A, Nguyen XV, Probyn L, Rajiah P, Sin J, Wasnik AP, Xu K. Noninterpretive Uses of Artificial Intelligence in Radiology. Acad Radiol 2021;28:1225-35. [PMID: 32059956 DOI: 10.1016/j.acra.2020.01.012] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 6.0] [Reference Citation Analysis]
617 Scherer J, Nolden M, Kleesiek J, Metzger J, Kades K, Schneider V, Bach M, Sedlaczek O, Bucher AM, Vogl TJ, Grünwald F, Kühn JP, Hoffmann RT, Kotzerke J, Bethge O, Schimmöller L, Antoch G, Müller HW, Daul A, Nikolaou K, la Fougère C, Kunz WG, Ingrisch M, Schachtner B, Ricke J, Bartenstein P, Nensa F, Radbruch A, Umutlu L, Forsting M, Seifert R, Herrmann K, Mayer P, Kauczor HU, Penzkofer T, Hamm B, Brenner W, Kloeckner R, Düber C, Schreckenberger M, Braren R, Kaissis G, Makowski M, Eiber M, Gafita A, Trager R, Weber WA, Neubauer J, Reisert M, Bock M, Bamberg F, Hennig J, Meyer PT, Ruf J, Haberkorn U, Schoenberg SO, Kuder T, Neher P, Floca R, Schlemmer HP, Maier-Hein K. Joint Imaging Platform for Federated Clinical Data Analytics. JCO Clin Cancer Inform 2020;4:1027-38. [PMID: 33166197 DOI: 10.1200/CCI.20.00045] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 6.0] [Reference Citation Analysis]
618 Le VH, Kha QH, Hung TNK, Le NQK. Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer. Cancers (Basel) 2021;13:3616. [PMID: 34298828 DOI: 10.3390/cancers13143616] [Reference Citation Analysis]
619 Li L, Su Q, Yang H. Preoperative prediction of microvascular invasion in hepatocellular carcinoma: a radiomic nomogram based on MRI. Clin Radiol 2021:S0009-9260(21)00576-6. [PMID: 34980458 DOI: 10.1016/j.crad.2021.12.008] [Reference Citation Analysis]
620 Lisini D, Lettieri S, Nava S, Accordino G, Frigerio S, Bortolotto C, Lancia A, Filippi AR, Agustoni F, Pandolfi L, Piloni D, Comoli P, Corsico AG, Stella GM. Local Therapies and Modulation of Tumor Surrounding Stroma in Malignant Pleural Mesothelioma: A Translational Approach. Int J Mol Sci 2021;22:9014. [PMID: 34445720 DOI: 10.3390/ijms22169014] [Reference Citation Analysis]
621 King RB, McMahon SJ, Hyland WB, Jain S, Butterworth KT, Prise KM, Hounsell AR, McGarry CK. An overview of current practice in external beam radiation oncology with consideration to potential benefits and challenges for nanotechnology. Cancer Nanotechnol 2017;8:3. [PMID: 28217177 DOI: 10.1186/s12645-017-0027-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.2] [Reference Citation Analysis]
622 Chen S, Jiang L, Zheng X, Shao J, Wang T, Zhang E, Gao F, Wang X, Zheng J. Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer. Cancer Sci 2021;112:2905-14. [PMID: 33931925 DOI: 10.1111/cas.14927] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
623 Kwon D, Reis IM, Breto AL, Tschudi Y, Gautney N, Zavala-Romero O, Lopez C, Ford JC, Punnen S, Pollack A, Stoyanova R. Classification of suspicious lesions on prostate multiparametric MRI using machine learning. J Med Imaging (Bellingham) 2018;5:034502. [PMID: 30840719 DOI: 10.1117/1.JMI.5.3.034502] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
624 Oh JH, Apte AP, Katsoulakis E, Riaz N, Hatzoglou V, Yu Y, Mahmood U, Veeraraghavan H, Pouryahya M, Iyer A, Shukla-Dave A, Tannenbaum A, Lee NY, Deasy JO. Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering. J Med Imaging (Bellingham) 2021;8:031904. [PMID: 33954225 DOI: 10.1117/1.JMI.8.3.031904] [Reference Citation Analysis]
625 Zou L, Yu S, Meng T, Zhang Z, Liang X, Xie Y. A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis. Comput Math Methods Med 2019;2019:6509357. [PMID: 31019547 DOI: 10.1155/2019/6509357] [Cited by in Crossref: 28] [Cited by in F6Publishing: 12] [Article Influence: 9.3] [Reference Citation Analysis]
626 Reuzé S, Orlhac F, Chargari C, Nioche C, Limkin E, Riet F, Escande A, Haie-Meder C, Dercle L, Gouy S, Buvat I, Deutsch E, Robert C. Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget 2017;8:43169-79. [PMID: 28574816 DOI: 10.18632/oncotarget.17856] [Cited by in Crossref: 59] [Cited by in F6Publishing: 47] [Article Influence: 14.8] [Reference Citation Analysis]
627 Li L, Wang K, Ma X, Liu Z, Wang S, Du J, Tian K, Zhou X, Wei W, Sun K, Lin Y, Wu Z, Tian J. Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma. Eur J Radiol 2019;118:81-7. [PMID: 31439263 DOI: 10.1016/j.ejrad.2019.07.006] [Cited by in Crossref: 20] [Cited by in F6Publishing: 17] [Article Influence: 6.7] [Reference Citation Analysis]
628 Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel) 2021;11:742. [PMID: 33919342 DOI: 10.3390/diagnostics11050742] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
629 de Farias EC, di Noia C, Han C, Sala E, Castelli M, Rundo L. Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features. Sci Rep 2021;11:21361. [PMID: 34725417 DOI: 10.1038/s41598-021-00898-z] [Reference Citation Analysis]
630 Shaikh F, Dupont-Roettger D, Dehmeshki J, Awan O, Kubassova O, Bisdas S. The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors. Front Oncol 2020;10:559946. [PMID: 33072586 DOI: 10.3389/fonc.2020.559946] [Reference Citation Analysis]
631 Mühlberg A, Museyko O, Laredo JD, Engelke K. A reproducible semi-automatic method to quantify the muscle-lipid distribution in clinical 3D CT images of the thigh. PLoS One 2017;12:e0175174. [PMID: 28453512 DOI: 10.1371/journal.pone.0175174] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
632 Wu J, Gong G, Cui Y, Li R. Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy. J Magn Reson Imaging 2016;44:1107-15. [PMID: 27080586 DOI: 10.1002/jmri.25279] [Cited by in Crossref: 77] [Cited by in F6Publishing: 64] [Article Influence: 12.8] [Reference Citation Analysis]
633 De Santis S, Moratal D, Canals S. Radiomicrobiomics: Advancing Along the Gut-brain Axis Through Big Data Analysis. Neuroscience 2019;403:145-9. [DOI: 10.1016/j.neuroscience.2017.11.055] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
634 AlRayahi J, Zapotocky M, Ramaswamy V, Hanagandi P, Branson H, Mubarak W, Raybaud C, Laughlin S. Pediatric Brain Tumor Genetics: What Radiologists Need to Know. Radiographics 2018;38:2102-22. [PMID: 30422762 DOI: 10.1148/rg.2018180109] [Cited by in Crossref: 18] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
635 Ghidini M, Vuozzo M, Galassi B, Mapelli P, Ceccarossi V, Caccamo L, Picchio M, Dondossola D. The Role of Positron Emission Tomography/Computed Tomography (PET/CT) for Staging and Disease Response Assessment in Localized and Locally Advanced Pancreatic Cancer. Cancers (Basel) 2021;13:4155. [PMID: 34439307 DOI: 10.3390/cancers13164155] [Reference Citation Analysis]
636 Zhang H, Mao Y, Chen X, Wu G, Liu X, Zhang P, Bai Y, Lu P, Yao W, Wang Y, Yu J, Zhang G. Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. Eur Radiol 2019;29:3358-71. [DOI: 10.1007/s00330-019-06124-9] [Cited by in Crossref: 29] [Cited by in F6Publishing: 23] [Article Influence: 9.7] [Reference Citation Analysis]
637 Folio LR, Machado LB, Dwyer AJ. Multimedia-enhanced Radiology Reports: Concept, Components, and Challenges. Radiographics 2018;38:462-82. [PMID: 29528822 DOI: 10.1148/rg.2017170047] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
638 Zinn PO, Singh SK, Kotrotsou A, Abrol S, Thomas G, Mosley J, Elakkad A, Hassan I, Kumar A, Colen RR. Distinct Radiomic Phenotypes Define Glioblastoma TP53-PTEN-EGFR Mutational Landscape. Neurosurgery 2017;64:203-10. [PMID: 28899058 DOI: 10.1093/neuros/nyx316] [Cited by in Crossref: 11] [Cited by in F6Publishing: 14] [Article Influence: 2.2] [Reference Citation Analysis]
639 Ge YX, Li J, Zhang JQ, Duan SF, Liu YK, Hu SD. Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values. J Xray Sci Technol 2020;28:285-97. [PMID: 32116286 DOI: 10.3233/XST-190614] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
640 Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ, Do RKG, Simpson AL. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys. 2018;45:5019-5029. [PMID: 30176047 DOI: 10.1002/mp.13159] [Cited by in Crossref: 40] [Cited by in F6Publishing: 39] [Article Influence: 10.0] [Reference Citation Analysis]
641 D'Amore B, Smolinski-Zhao S, Daye D, Uppot RN. Role of Machine Learning and Artificial Intelligence in Interventional Oncology. Curr Oncol Rep 2021;23:70. [PMID: 33880651 DOI: 10.1007/s11912-021-01054-6] [Reference Citation Analysis]
642 Liu W, Liu X, Peng M, Chen GQ, Liu PH, Cui XW, Jiang F, Dietrich CF. Artificial intelligence for hepatitis evaluation. World J Gastroenterol 2021; 27(34): 5715-5726 [PMID: 34629796 DOI: 10.3748/wjg.v27.i34.5715] [Reference Citation Analysis]
643 Shakir H, Deng Y, Rasheed H, Khan TMR. Radiomics based likelihood functions for cancer diagnosis. Sci Rep 2019;9:9501. [PMID: 31263186 DOI: 10.1038/s41598-019-45053-x] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
644 Brodie A, Dai N, Teoh JY, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021;39:379-99. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Reference Citation Analysis]
645 Lee H, Nguyen A, Ki SY, Lee JE, Do L, Park MH, Lee JS, Kim HJ, Park I, Lim HS. Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning. Front Oncol 2021;11:744460. [DOI: 10.3389/fonc.2021.744460] [Reference Citation Analysis]
646 Yaşar S, Voyvoda N, Voyvoda B, Özer T. Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma. Abdom Radiol (NY) 2020;45:3821-30. [PMID: 32253464 DOI: 10.1007/s00261-020-02495-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
647 Pei Q, Yi X, Chen C, Pang P, Fu Y, Lei G, Chen C, Tan F, Gong G, Li Q, Zai H, Chen BT. Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer. Eur Radiol 2021. [PMID: 34258636 DOI: 10.1007/s00330-021-08167-3] [Reference Citation Analysis]
648 Chen Y, Lu L, Zhu T, Ma D. Technical overview of magnetic resonance fingerprinting and its applications in radiation therapy. Med Phys 2021. [PMID: 34633687 DOI: 10.1002/mp.15254] [Reference Citation Analysis]
649 Gillies RJ, Balagurunathan Y. Perfusion MR Imaging of Breast Cancer: Insights Using "Habitat Imaging". Radiology 2018;288:36-7. [PMID: 29714676 DOI: 10.1148/radiol.2018180271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
650 Leithner D, Horvat JV, Ochoa-Albiztegui RE, Thakur S, Wengert G, Morris EA, Helbich TH, Pinker K. Imaging and the completion of the omics paradigm in breast cancer. Radiologe 2018;58:7-13. [PMID: 29947931 DOI: 10.1007/s00117-018-0409-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
651 Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021;13:97-104. [PMID: 34513082 DOI: 10.17691/stm2021.13.2.11] [Reference Citation Analysis]
652 Hormuth DA 2nd, Sorace AG, Virostko J, Abramson RG, Bhujwalla ZM, Enriquez-Navas P, Gillies R, Hazle JD, Mason RP, Quarles CC, Weis JA, Whisenant JG, Xu J, Yankeelov TE. Translating preclinical MRI methods to clinical oncology. J Magn Reson Imaging 2019;50:1377-92. [PMID: 30925001 DOI: 10.1002/jmri.26731] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
653 Zhang MZ, Ou-Yang HQ, Liu JF, Jin D, Wang CJ, Ni M, Liu XG, Lang N, Jiang L, Yuan HS. Predicting postoperative recovery in cervical spondylotic myelopathy: construction and interpretation of T2*-weighted radiomic-based extra trees models. Eur Radiol 2022. [PMID: 35024949 DOI: 10.1007/s00330-021-08383-x] [Reference Citation Analysis]
654 Duron L, Balvay D, Vande Perre S, Bouchouicha A, Savatovsky J, Sadik JC, Thomassin-Naggara I, Fournier L, Lecler A. Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One 2019;14:e0213459. [PMID: 30845221 DOI: 10.1371/journal.pone.0213459] [Cited by in Crossref: 53] [Cited by in F6Publishing: 48] [Article Influence: 17.7] [Reference Citation Analysis]
655 Saint Martin MJ, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. MAGMA 2021;34:355-66. [PMID: 33180226 DOI: 10.1007/s10334-020-00892-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
656 Strijbis VIJ, de Bloeme CM, Jansen RW, Kebiri H, Nguyen HG, de Jong MC, Moll AC, Bach-Cuadra M, de Graaf P, Steenwijk MD. Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma. Sci Rep 2021;11:14590. [PMID: 34272413 DOI: 10.1038/s41598-021-93905-2] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
657 Ye K, Chen M, Zhu Q, Lu Y, Yuan H. Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules. Quant Imaging Med Surg 2021;11:2344-53. [PMID: 34079706 DOI: 10.21037/qims-20-932] [Reference Citation Analysis]
658 Hu T, Wang S, E X, Yuan Y, Huang L, Wang J, Shi D, Li Y, Peng W, Tong T. CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules. Front Oncol 2019;9:1241. [PMID: 31803619 DOI: 10.3389/fonc.2019.01241] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
659 Lee SJ, Pickhardt PJ. Opportunistic Screening for Osteoporosis Using Body CT Scans Obtained for Other Indications: the UW Experience. Clinic Rev Bone Miner Metab 2017;15:128-37. [DOI: 10.1007/s12018-017-9235-7] [Cited by in Crossref: 19] [Cited by in F6Publishing: 8] [Article Influence: 3.8] [Reference Citation Analysis]
660 Zhang L, Zhou H, Gu D, Tian J, Zhang B, Dong D, Mo X, Liu J, Luo X, Pei S, Dong Y, Huang W, Chen Q, Liang C, Lian Z, Zhang S. Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging. J Cancer 2019;10:4217-25. [PMID: 31413740 DOI: 10.7150/jca.33345] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 5.0] [Reference Citation Analysis]
661 Brindle KM, Izquierdo-garcía JL, Lewis DY, Mair RJ, Wright AJ. Brain Tumor Imaging. JCO 2017;35:2432-8. [DOI: 10.1200/jco.2017.72.7636] [Cited by in Crossref: 26] [Cited by in F6Publishing: 13] [Article Influence: 5.2] [Reference Citation Analysis]
662 Hatt M, Tixier F, Visvikis D, Cheze Le Rest C. Radiomics in PET/CT: More Than Meets the Eye? J Nucl Med 2017;58:365-6. [PMID: 27811126 DOI: 10.2967/jnumed.116.184655] [Cited by in Crossref: 52] [Cited by in F6Publishing: 41] [Article Influence: 8.7] [Reference Citation Analysis]
663 Xie T, Zhao Q, Fu C, Grimm R, Gu Y, Peng W. Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm). Eur Radiol 2021. [PMID: 34505195 DOI: 10.1007/s00330-021-08244-7] [Reference Citation Analysis]
664 Wu YP, Lin YS, Wu WG, Yang C, Gu JQ, Bai Y, Wang MY. Semiautomatic Segmentation of Glioma on Mobile Devices. J Healthc Eng 2017;2017:8054939. [PMID: 29065648 DOI: 10.1155/2017/8054939] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.2] [Reference Citation Analysis]
665 Chen X, Fang M, Dong D, Liu L, Xu X, Wei X, Jiang X, Qin L, Liu Z. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme. Acad Radiol 2019;26:1292-300. [PMID: 30660472 DOI: 10.1016/j.acra.2018.12.016] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 4.7] [Reference Citation Analysis]
666 Zhang W, Fang M, Dong D, Wang X, Ke X, Zhang L, Hu C, Guo L, Guan X, Zhou J, Shan X, Tian J. Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer. Radiotherapy and Oncology 2020;145:13-20. [DOI: 10.1016/j.radonc.2019.11.023] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 8.5] [Reference Citation Analysis]
667 Kimura M, Kato I, Ishibashi K, Sone Y, Nagao T, Umemura M. Texture Analysis Using Preoperative Positron Emission Tomography Images May Predict the Prognosis of Patients With Resectable Oral Squamous Cell Carcinoma. J Oral Maxillofac Surg 2021;79:1168-76. [PMID: 33428864 DOI: 10.1016/j.joms.2020.12.014] [Reference Citation Analysis]
668 Huang Z, Lyu M, Ai Z, Chen Y, Liang Y, Xiang Z. Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features. Front Surg 2021;8:736737. [PMID: 34733879 DOI: 10.3389/fsurg.2021.736737] [Reference Citation Analysis]
669 Ouyang ML, Wang YR, Deng QS, Zhu YF, Zhao ZH, Wang L, Wang LX, Tang K. Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal-Hilar Lymph Nodes in Non-Small-Cell Lung Cancer. Front Oncol 2021;11:710909. [PMID: 34568038 DOI: 10.3389/fonc.2021.710909] [Reference Citation Analysis]
670 Yu FH, Wang JX, Ye XH, Deng J, Hang J, Yang B. Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 2019;119:108658. [PMID: 31521878 DOI: 10.1016/j.ejrad.2019.108658] [Cited by in Crossref: 25] [Cited by in F6Publishing: 24] [Article Influence: 8.3] [Reference Citation Analysis]
671 Somasundaram E, Litzler A, Wadhwa R, Owen S, Scott J. Persistent homology of tumor CT scans is associated with survival in lung cancer. Med Phys 2021;48:7043-51. [PMID: 34587294 DOI: 10.1002/mp.15255] [Reference Citation Analysis]
672 Zhuo Y, Feng M, Yang S, Zhou L, Ge D, Lu S, Liu L, Shan F, Zhang Z. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol 2020;13:100820. [PMID: 32622312 DOI: 10.1016/j.tranon.2020.100820] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
673 Santiago R, Ortiz Jimenez J, Forghani R, Muthukrishnan N, Del Corpo O, Karthigesu S, Haider MY, Reinhold C, Assouline S. CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma. Transl Oncol 2021;14:101188. [PMID: 34343854 DOI: 10.1016/j.tranon.2021.101188] [Reference Citation Analysis]
674 Sagir Kahraman A. Radiomics in Hepatocellular Carcinoma. J Gastrointest Cancer 2020;51:1165-8. [PMID: 32844349 DOI: 10.1007/s12029-020-00493-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
675 Porter E, Roussakis AA, Lao-Kaim NP, Piccini P. Multimodal dopamine transporter (DAT) imaging and magnetic resonance imaging (MRI) to characterise early Parkinson's disease. Parkinsonism Relat Disord 2020;79:26-33. [PMID: 32861103 DOI: 10.1016/j.parkreldis.2020.08.010] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
676 Ma L, Xiao Z, Li K, Li S, Li J, Yi X. Game theoretic interpretability for learning based preoperative gliomas grading. Future Generation Computer Systems 2020;112:1-10. [DOI: 10.1016/j.future.2020.04.038] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
677 Parr E, Du Q, Zhang C, Lin C, Kamal A, McAlister J, Liang X, Bavitz K, Rux G, Hollingsworth M, Baine M, Zheng D. Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy. Cancers (Basel) 2020;12:E1051. [PMID: 32344538 DOI: 10.3390/cancers12041051] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
678 Clancy K, Dadashzadeh ER, Handzel R, Rieser C, Moses JB, Rosenblum L, Wu S. Machine learning for the prediction of pathologic pneumatosis intestinalis. Surgery 2021;170:797-805. [PMID: 33926706 DOI: 10.1016/j.surg.2021.03.049] [Reference Citation Analysis]
679 Wong CW, Chaudhry A. Radiogenomics of lung cancer. J Thorac Dis 2020;12:5104-9. [PMID: 33145087 DOI: 10.21037/jtd-2019-pitd-10] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
680 Fletcher JG, Leng S, Yu L, Mccollough CH. Dealing with Uncertainty in CT Images. Radiology 2016;279:5-10. [DOI: 10.1148/radiol.2016152771] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 2.7] [Reference Citation Analysis]
681 Stojanovska J. Is It a Cardiac Tumor or a Thrombus: An Everlasting Dilemma solved by Radiomics Analysis. Acad Radiol 2021:S1076-6332(21)00533-X. [PMID: 34961657 DOI: 10.1016/j.acra.2021.11.006] [Reference Citation Analysis]
682 Bianconi F, Palumbo I, Spanu A, Nuvoli S, Fravolini ML, Palumbo B. PET/CT Radiomics in Lung Cancer: An Overview. Applied Sciences 2020;10:1718. [DOI: 10.3390/app10051718] [Cited by in Crossref: 10] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
683 Zhang X, Liang M, Yang Z, Zheng C, Wu J, Ou B, Li H, Wu X, Luo B, Shen J. Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification. Front Oncol 2020;10:1621. [PMID: 32984032 DOI: 10.3389/fonc.2020.01621] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
684 Qin Q, Shi A, Zhang R, Wen Q, Niu T, Chen J, Qiu Q, Wan Y, Sun X, Xing L. Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. Thorac Cancer 2020;11:964-72. [PMID: 32061061 DOI: 10.1111/1759-7714.13349] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
685 Xiao F, Sun R, Sun W, Xu D, Lan L, Li H, Liu H, Xu H. Radiomics analysis of chest CT to predict the overall survival for the severe patients of COVID-19 pneumonia. Phys Med Biol 2021;66. [PMID: 33845467 DOI: 10.1088/1361-6560/abf717] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
686 Yamashita R, Perrin T, Chakraborty J, Chou JF, Horvat N, Koszalka MA, Midya A, Gonen M, Allen P, Jarnagin WR, Simpson AL, Do RKG. Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol 2020;30:195-205. [PMID: 31392481 DOI: 10.1007/s00330-019-06381-8] [Cited by in Crossref: 22] [Cited by in F6Publishing: 15] [Article Influence: 7.3] [Reference Citation Analysis]
687 He S, Wu J, Lian C, Gach HM, Mutic S, Bosch W, Michalski J, Li H. An Adaptive Low-Rank Modeling-Based Active Learning Method for Medical Image Annotation. Ing Rech Biomed 2021;42:334-44. [PMID: 34934476 DOI: 10.1016/j.irbm.2020.06.001] [Reference Citation Analysis]
688 Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, Danesi R. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging 2018;9:915-24. [PMID: 30430428 DOI: 10.1007/s13244-018-0657-7] [Cited by in Crossref: 26] [Cited by in F6Publishing: 22] [Article Influence: 6.5] [Reference Citation Analysis]
689 Wang J, Yang F, Liu W, Sun J, Han Y, Li D, Gkoutos GV, Zhu Y, Chen Y. Radiomic Analysis of Native T1 Mapping Images Discriminates Between MYH7 and MYBPC3-Related Hypertrophic Cardiomyopathy. J Magn Reson Imaging 2020;52:1714-21. [PMID: 32525266 DOI: 10.1002/jmri.27209] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
690 Wu W, Li D, Du J, Gao X, Gu W, Zhao F, Feng X, Yan H. An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm. Comput Math Methods Med 2020;2020:6789306. [PMID: 32733596 DOI: 10.1155/2020/6789306] [Cited by in Crossref: 8] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
691 Luo P, Fang Z, Zhang P, Yang Y, Zhang H, Su L, Wang Z, Ren J. Radiomics Score Combined with ACR TI-RADS in Discriminating Benign and Malignant Thyroid Nodules Based on Ultrasound Images: A Retrospective Study. Diagnostics (Basel) 2021;11:1011. [PMID: 34205943 DOI: 10.3390/diagnostics11061011] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
692 Bani-Sadr A, Eker OF, Berner LP, Ameli R, Hermier M, Barritault M, Meyronet D, Guyotat J, Jouanneau E, Honnorat J, Ducray F, Berthezene Y. Conventional MRI radiomics in patients with suspected early- or pseudo-progression. Neurooncol Adv 2019;1:vdz019. [PMID: 32642655 DOI: 10.1093/noajnl/vdz019] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.7] [Reference Citation Analysis]
693 Yang X, Liu M, Ren Y, Chen H, Yu P, Wang S, Zhang R, Dai H, Wang C. Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients-a radiomics nomogram analysis. Eur Radiol 2021. [PMID: 34807270 DOI: 10.1007/s00330-021-08366-y] [Reference Citation Analysis]
694 Limkin EJ, Reuzé S, Carré A, Sun R, Schernberg A, Alexis A, Deutsch E, Ferté C, Robert C. The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features. Sci Rep 2019;9:4329. [PMID: 30867443 DOI: 10.1038/s41598-019-40437-5] [Cited by in Crossref: 25] [Cited by in F6Publishing: 23] [Article Influence: 8.3] [Reference Citation Analysis]
695 Pan ZQ, Zhang SJ, Wang XL, Jiao YX, Qiu JJ. Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma. Behav Neurol 2020;2020:1712604. [PMID: 33163122 DOI: 10.1155/2020/1712604] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
696 Zhang LL, Huang MY, Li Y, Liang JH, Gao TS, Deng B, Yao JJ, Lin L, Chen FP, Huang XD, Kou J, Li CF, Xie CM, Lu Y, Sun Y. Pretreatment MRI radiomics analysis allows for reliable prediction of local recurrence in non-metastatic T4 nasopharyngeal carcinoma. EBioMedicine 2019;42:270-80. [PMID: 30928358 DOI: 10.1016/j.ebiom.2019.03.050] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 6.0] [Reference Citation Analysis]
697 Kim BS, Kim ST, Kim JH, Seol HJ, Nam D, Shin HJ, Lee J, Kong D. Apparent Diffusion Coefficient as a Predictive Biomarker for Survival in Patients with Treatment-Naive Glioblastoma Using Quantitative Multiparametric Magnetic Resonance Profiling. World Neurosurgery 2019;122:e812-20. [DOI: 10.1016/j.wneu.2018.10.151] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
698 Alvi E, Gupta R, Borok RZ, Escobar-Hoyos L, Shroyer KR. Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI. J Magn Reson Imaging 2020;51:341-54. [PMID: 31041822 DOI: 10.1002/jmri.26763] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
699 Vallières M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Aerts HJWL, Khaouam N, Nguyen-Tan PF, Wang CS, Sultanem K, Seuntjens J, El Naqa I. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 2017;7:10117. [PMID: 28860628 DOI: 10.1038/s41598-017-10371-5] [Cited by in Crossref: 173] [Cited by in F6Publishing: 143] [Article Influence: 34.6] [Reference Citation Analysis]
700 Mattonen SA, Davidzon GA, Benson J, Leung ANC, Vasanawala M, Horng G, Shrager JB, Napel S, Nair VS. Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology 2019;293:451-9. [PMID: 31526257 DOI: 10.1148/radiol.2019190357] [Cited by in Crossref: 18] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
701 Petresc B, Lebovici A, Caraiani C, Feier DS, Graur F, Buruian MM. Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study. Cancers (Basel) 2020;12:E1894. [PMID: 32674345 DOI: 10.3390/cancers12071894] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
702 Hainc N, Stippich C, Stieltjes B, Leu S, Bink A. Experimental Texture Analysis in Glioblastoma: A Methodological Study. Invest Radiol 2017;52:367-73. [PMID: 28230716 DOI: 10.1097/RLI.0000000000000354] [Cited by in Crossref: 19] [Cited by in F6Publishing: 11] [Article Influence: 4.8] [Reference Citation Analysis]
703 Rizzo S, Raimondi S, de Jong EEC, van Elmpt W, De Piano F, Petrella F, Bagnardi V, Jochems A, Bellomi M, Dingemans AM, Lambin P. Genomics of non-small cell lung cancer (NSCLC): Association between CT-based imaging features and EGFR and K-RAS mutations in 122 patients-An external validation. Eur J Radiol. 2019;110:148-155. [PMID: 30599853 DOI: 10.1016/j.ejrad.2018.11.032] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 2.3] [Reference Citation Analysis]
704 Dou TH, Coroller TP, van Griethuysen JJM, Mak RH, Aerts HJWL. Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLoS One 2018;13:e0206108. [PMID: 30388114 DOI: 10.1371/journal.pone.0206108] [Cited by in Crossref: 37] [Cited by in F6Publishing: 37] [Article Influence: 9.3] [Reference Citation Analysis]
705 Zhuang Z, Zhang Y, Wei M, Yang X, Wang Z. Magnetic Resonance Imaging Evaluation of the Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2021;11:709070. [PMID: 34327144 DOI: 10.3389/fonc.2021.709070] [Reference Citation Analysis]
706 Sollini M, Bartoli F, Cavinato L, Ieva F, Ragni A, Marciano A, Zanca R, Galli L, Paiar F, Pasqualetti F, Erba PA. [18F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer. EJNMMI Res 2021;11:119. [PMID: 34837532 DOI: 10.1186/s13550-021-00858-8] [Reference Citation Analysis]
707 Jayaprakasam VS, Paroder V, Gibbs P, Bajwa R, Gangai N, Sosa RE, Petkovska I, Golia Pernicka JS, Fuqua JL 3rd, Bates DDB, Weiser MR, Cercek A, Gollub MJ. MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. Eur Radiol 2021. [PMID: 34327580 DOI: 10.1007/s00330-021-08144-w] [Reference Citation Analysis]
708 Capobianco E. High-dimensional role of AI and machine learning in cancer research. Br J Cancer. [DOI: 10.1038/s41416-021-01689-z] [Reference Citation Analysis]
709 Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-762. [PMID: 28975929 DOI: 10.1038/nrclinonc.2017.141] [Cited by in Crossref: 1063] [Cited by in F6Publishing: 990] [Article Influence: 212.6] [Reference Citation Analysis]
710 Sun Y, Bai H, Xia W, Wang D, Zhou B, Zhao X, Yang G, Xu L, Zhang W, Liu P, Xu J, Meng S, Liu R, Gao X. Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI. J Magn Reson Imaging 2020;52:1083-90. [PMID: 32233054 DOI: 10.1002/jmri.27143] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
711 Bach Cuadra M, Favre J, Omoumi P. Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics. Semin Musculoskelet Radiol 2020;24:50-64. [DOI: 10.1055/s-0039-3400268] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
712 Ou J, Wu L, Li R, Wu CQ, Liu J, Chen TW, Zhang XM, Tang S, Wu YP, Yang LQ, Tan BG, Lu FL. CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study. Quant Imaging Med Surg 2021;11:628-40. [PMID: 33532263 DOI: 10.21037/qims-20-241] [Reference Citation Analysis]
713 Wang K, Zhou Z, Wang R, Chen L, Zhang Q, Sher D, Wang J. A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer. Med Phys 2020;47:5392-400. [DOI: 10.1002/mp.14388] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
714 Chen B, Zhang R, Gan Y, Yang L, Li W. Development and clinical application of radiomics in lung cancer. Radiat Oncol 2017;12:154. [PMID: 28915902 DOI: 10.1186/s13014-017-0885-x] [Cited by in Crossref: 32] [Cited by in F6Publishing: 31] [Article Influence: 6.4] [Reference Citation Analysis]
715 Shukla M, Forghani R, Agarwal M. Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging. Neuroimaging Clin N Am 2020;30:341-57. [PMID: 32600635 DOI: 10.1016/j.nic.2020.04.005] [Reference Citation Analysis]
716 Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, Thomas KE, Thomas S, Akoumianakis I, Fan LM, Kesavan S, Herdman L, Alashi A, Centeno EH, Lyasheva M, Griffin BP, Flamm SD, Shirodaria C, Sabharwal N, Kelion A, Dweck MR, Van Beek EJR, Deanfield J, Hopewell JC, Neubauer S, Channon KM, Achenbach S, Newby DE, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J. 2019;40:3529-3543. [PMID: 31504423 DOI: 10.1093/eurheartj/ehz592] [Cited by in Crossref: 82] [Cited by in F6Publishing: 63] [Article Influence: 41.0] [Reference Citation Analysis]
717 Castello A, Lopci E. Update on tumor metabolism and patterns of response to immunotherapy. Q J Nucl Med Mol Imaging 2020;64. [DOI: 10.23736/s1824-4785.20.03251-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
718 Sha X, Gong G, Qiu Q, Duan J, Li D, Yin Y. Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging. BMC Med Imaging 2020;20:12. [PMID: 32024469 DOI: 10.1186/s12880-020-0416-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
719 Mannil M, Burgstaller JM, Thanabalasingam A, Winklhofer S, Betz M, Held U, Guggenberger R. Texture analysis of paraspinal musculature in MRI of the lumbar spine: analysis of the lumbar stenosis outcome study (LSOS) data. Skeletal Radiol 2018;47:947-54. [PMID: 29497775 DOI: 10.1007/s00256-018-2919-3] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 4.0] [Reference Citation Analysis]
720 Willemink MJ, Varga-Szemes A, Schoepf UJ, Codari M, Nieman K, Fleischmann D, Mastrodicasa D. Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence. Eur Radiol Exp 2021;5:12. [PMID: 33763754 DOI: 10.1186/s41747-021-00207-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
721 Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C. Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Radiology. 2019;290:290-297. [PMID: 30422086 DOI: 10.1148/radiol.2018181352] [Cited by in Crossref: 50] [Cited by in F6Publishing: 50] [Article Influence: 12.5] [Reference Citation Analysis]
722 Tagliafico AS, Bignotti B, Rossi F, Valdora F, Martinoli C. Local recurrence of soft tissue sarcoma: a radiomic analysis. Radiol Oncol 2019;53:300-6. [PMID: 31553702 DOI: 10.2478/raon-2019-0041] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
723 Machicado JD, Koay EJ, Krishna SG. Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions. Diagnostics (Basel) 2020;10:E505. [PMID: 32708348 DOI: 10.3390/diagnostics10070505] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
724 Wang S, Sun Y, Li R, Mao N, Li Q, Jiang T, Chen Q, Duan S, Xie H, Gu Y. Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions. Eur Radiol 2021. [PMID: 34189600 DOI: 10.1007/s00330-021-08134-y] [Reference Citation Analysis]
725 Li Z, Guo J, Xu X, Wei W, Xian J. MRI-based radiomics model can improve the predictive performance of postlaminar optic nerve invasion in retinoblastoma. Br J Radiol 2021;:20211027. [PMID: 34826253 DOI: 10.1259/bjr.20211027] [Reference Citation Analysis]
726 Hassani C, Varghese BA, Nieva J, Duddalwar V. Radiomics in Pulmonary Lesion Imaging. American Journal of Roentgenology 2019;212:497-504. [DOI: 10.2214/ajr.18.20623] [Cited by in Crossref: 19] [Cited by in F6Publishing: 11] [Article Influence: 6.3] [Reference Citation Analysis]
727 Nougaret S, Tardieu M, Vargas H, Reinhold C, Vande Perre S, Bonanno N, Sala E, Thomassin-naggara I. Ovarian cancer: An update on imaging in the era of radiomics. Diagnostic and Interventional Imaging 2019;100:647-55. [DOI: 10.1016/j.diii.2018.11.007] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 10.3] [Reference Citation Analysis]
728 Cao JM, Yang JQ, Ming ZQ, Wu JL, Yang LQ, Chen TW, Li R, Ou J, Zhang XM, Mu QW, Li HJ, Hu J. A radiomics model of liver CT to predict risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis.Eur J Radiol. 2020;130:109201. [PMID: 32738462 DOI: 10.1016/j.ejrad.2020.109201] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
729 Pena E, Ojiaku M, Inacio JR, Gupta A, Macdonald DB, Shabana W, Seely JM, Rybicki FJ, Dennie C, Thornhill RE. Can CT and MR Shape and Textural Features Differentiate Benign Versus Malignant Pleural Lesions? Academic Radiology 2017;24:1277-87. [DOI: 10.1016/j.acra.2017.03.006] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 3.6] [Reference Citation Analysis]
730 Kagiyama N, Shrestha S, Cho JS, Khalil M, Singh Y, Challa A, Casaclang-Verzosa G, Sengupta PP. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine 2020;54:102726. [PMID: 32268274 DOI: 10.1016/j.ebiom.2020.102726] [Cited by in Crossref: 9]