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For: Ford J, Dogan N, Young L, Yang F. Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain. Contrast Media Mol Imaging 2018;2018:1729071. [PMID: 30154684 DOI: 10.1155/2018/1729071] [Cited by in Crossref: 43] [Cited by in F6Publishing: 40] [Article Influence: 10.8] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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]
3 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]
4 Iv M, Zhou M, Shpanskaya K, Perreault S, Wang Z, Tranvinh E, Lanzman B, Vajapeyam S, Vitanza NA, Fisher PG, Cho YJ, Laughlin S, Ramaswamy V, Taylor MD, Cheshier SH, Grant GA, Young Poussaint T, Gevaert O, Yeom KW. MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma. AJNR Am J Neuroradiol 2019;40:154-61. [PMID: 30523141 DOI: 10.3174/ajnr.A5899] [Cited by in Crossref: 42] [Cited by in F6Publishing: 22] [Article Influence: 10.5] [Reference Citation Analysis]
5 Shiri I, Hajianfar G, Sohrabi A, Abdollahi H, P Shayesteh S, Geramifar P, Zaidi H, Oveisi M, Rahmim A. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses. Med Phys 2020;47:4265-80. [PMID: 32615647 DOI: 10.1002/mp.14368] [Cited by in Crossref: 22] [Cited by in F6Publishing: 16] [Article Influence: 11.0] [Reference Citation Analysis]
6 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]
7 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]
8 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]
9 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]
10 Schmidt RM, Delgadillo R, Ford JC, Padgett KR, Studenski M, Abramowitz MC, Spieler B, Xu Y, Yang F, Dogan N. Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer. Sci Rep 2021;11:22737. [PMID: 34815464 DOI: 10.1038/s41598-021-02154-w] [Reference Citation Analysis]
11 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]
12 Kovalska M, Hnilicova P, Kalenska D, Tothova B, Adamkov M, Lehotsky J. Effect of Methionine Diet on Metabolic and Histopathological Changes of Rat Hippocampus. Int J Mol Sci 2019;20:E6234. [PMID: 31835644 DOI: 10.3390/ijms20246234] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
13 Singh G, Manjila S, Sakla N, True A, Wardeh AH, Beig N, Vaysberg A, Matthews J, Prasanna P, Spektor V. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021;125:641-57. [PMID: 33958734 DOI: 10.1038/s41416-021-01387-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
14 Carré A, Klausner G, Edjlali M, Lerousseau M, Briend-Diop J, Sun R, Ammari S, Reuzé S, Alvarez Andres E, Estienne T, Niyoteka S, Battistella E, Vakalopoulou M, Dhermain F, Paragios N, Deutsch E, Oppenheim C, Pallud J, Robert C. Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. Sci Rep 2020;10:12340. [PMID: 32704007 DOI: 10.1038/s41598-020-69298-z] [Cited by in Crossref: 27] [Cited by in F6Publishing: 23] [Article Influence: 13.5] [Reference Citation Analysis]
15 Yang F, Young LA, Johnson PB. Quantitative radiomics: Validating image textural features for oncological PET in lung cancer. Radiother Oncol 2018;129:209-17. [PMID: 30279049 DOI: 10.1016/j.radonc.2018.09.009] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 2.8] [Reference Citation Analysis]
16 Rich B, Huang J, Yang Y, Jin W, Johnson P, Wang L, Yang F. Radiomics Predicts for Distant Metastasis in Locally Advanced Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma. Cancers (Basel) 2021;13:5689. [PMID: 34830844 DOI: 10.3390/cancers13225689] [Reference Citation Analysis]
17 Lee J, Steinmann A, Ding Y, Lee H, Owens C, Wang J, Yang J, Followill D, Ger R, MacKin D, Court LE. Radiomics feature robustness as measured using an MRI phantom. Sci Rep 2021;11:3973. [PMID: 33597610 DOI: 10.1038/s41598-021-83593-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
18 Eck B, Chirra PV, Muchhala A, Hall S, Bera K, Tiwari P, Madabhushi A, Seiberlich N, Viswanath SE. Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters. J Magn Reson Imaging 2021;54:1009-21. [PMID: 33860966 DOI: 10.1002/jmri.27635] [Reference Citation Analysis]
19 Bianchini L, Botta F, Origgi D, Rizzo S, Mariani M, Summers P, García-Polo P, Cremonesi M, Lascialfari A. PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis. Phys Med 2020;71:71-81. [PMID: 32092688 DOI: 10.1016/j.ejmp.2020.02.003] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 6.0] [Reference Citation Analysis]
20 Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019;104:302-15. [PMID: 30711529 DOI: 10.1016/j.ijrobp.2019.01.087] [Cited by in Crossref: 18] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
21 van Houdt PJ, Yang Y, van der Heide UA. Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy. Front Oncol 2020;10:615643. [PMID: 33585242 DOI: 10.3389/fonc.2020.615643] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
22 Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019;9:1303-1322. [PMID: 30867832 DOI: 10.7150/thno.30309] [Cited by in Crossref: 149] [Cited by in F6Publishing: 146] [Article Influence: 49.7] [Reference Citation Analysis]
23 Crombé A, Fadli D, Buy X, Italiano A, Saut O, Kind M. High-Grade Soft-Tissue Sarcomas: Can Optimizing Dynamic Contrast-Enhanced MRI Postprocessing Improve Prognostic Radiomics Models? J Magn Reson Imaging 2020;52:282-97. [PMID: 31922323 DOI: 10.1002/jmri.27040] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
24 Simpson G, Ford JC, Llorente R, Portelance L, Yang F, Mellon EA, Dogan N. Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features. Phys Med 2020;80:209-20. [PMID: 33190077 DOI: 10.1016/j.ejmp.2020.10.029] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
25 He Y, Rong Y, Chen H, Zhang Z, Qiu J, Zheng L, Benedict S, Niu X, Pan N, Liu Y, Yuan Z. Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer. Acta Radiol 2020;61:568-76. [DOI: 10.1177/0284185119870157] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
26 Crombé A, Kind M, Fadli D, Le Loarer F, Italiano A, Buy X, Saut O. Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients. Sci Rep 2020;10:15496. [PMID: 32968131 DOI: 10.1038/s41598-020-72535-0] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
27 Hassani C, Saremi F, Varghese BA, Duddalwar V. Myocardial Radiomics in Cardiac MRI. AJR Am J Roentgenol 2020;214:536-45. [PMID: 31799865 DOI: 10.2214/AJR.19.21986] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
28 Mao H, Zhang B, Zou M, Huang Y, Yang L, Wang C, Pang P, Zhao Z. MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors. Front Oncol 2021;11:631927. [PMID: 34041017 DOI: 10.3389/fonc.2021.631927] [Reference Citation Analysis]
29 Delgadillo R, Spieler BO, Ford JC, Kwon D, Yang F, Studenski M, Padgett KR, Abramowitz MC, Dal Pra A, Stoyanova R, Pollack A, Dogan N. Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer. Med Phys 2021;48:2386-99. [PMID: 33598943 DOI: 10.1002/mp.14787] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
30 Masokano IB, Liu W, Xie S, Marcellin DFH, Pei Y, Li W. The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges. Cancer Imaging 2020;20:67. [PMID: 32962762 DOI: 10.1186/s40644-020-00341-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
31 Simpson G, Spieler B, Dogan N, Portelance L, Mellon EA, Kwon D, Ford JC, Yang F. Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: A pilot study. Med Phys 2020;47:3682-90. [DOI: 10.1002/mp.14200] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
32 Ehret F, Kaul D, Clusmann H, Delev D, Kernbach JM. Machine Learning-Based Radiomics in Neuro-Oncology. Acta Neurochir Suppl 2022;134:139-51. [PMID: 34862538 DOI: 10.1007/978-3-030-85292-4_18] [Reference Citation Analysis]
33 Crombé A, Buy X, Han F, Toupin S, Kind M. Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping-Based Radiomics Features: A Comparative Study. J Magn Reson Imaging 2021;54:537-48. [PMID: 33594768 DOI: 10.1002/jmri.27558] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D'Amico NC, Sardanelli F. AI applications to medical images: From machine learning to deep learning. Phys Med 2021;83:9-24. [PMID: 33662856 DOI: 10.1016/j.ejmp.2021.02.006] [Cited by in Crossref: 4] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
35 Soni N, Priya S, Bathla G. Texture Analysis in Cerebral Gliomas: A Review of the Literature. AJNR Am J Neuroradiol 2019;40:928-34. [PMID: 31122918 DOI: 10.3174/ajnr.A6075] [Cited by in Crossref: 36] [Cited by in F6Publishing: 21] [Article Influence: 12.0] [Reference Citation Analysis]
36 Joo L, Jung SC, Lee H, Park SY, Kim M, Park JE, Choi KM. Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patients. Sci Rep 2021;11:17143. [PMID: 34433881 DOI: 10.1038/s41598-021-96621-z] [Reference Citation Analysis]
37 Dercle L, Henry T, Carré A, Paragios N, Deutsch E, Robert C. Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods 2021;188:44-60. [PMID: 32697964 DOI: 10.1016/j.ymeth.2020.07.003] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
38 Li Q, Xiao Q, Li J, Duan S, Wang H, Gu Y. MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC. Cancer Manag Res 2020;12:10603-13. [PMID: 33149669 DOI: 10.2147/CMAR.S271876] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
39 Zheng R, Shi C, Wang C, Shi N, Qiu T, Chen W, Shi Y, Wang H. Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI. Biomolecules 2021;11:307. [PMID: 33670596 DOI: 10.3390/biom11020307] [Reference Citation Analysis]
40 Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M. Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging 2019;50:1773-88. [DOI: 10.1002/jmri.26753] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
41 Li XT, Huang RY. Standardization of imaging methods for machine learning in neuro-oncology. Neurooncol Adv 2020;2:iv49-55. [PMID: 33521640 DOI: 10.1093/noajnl/vdaa054] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
42 Chen H, He Y, Zhao C, Zheng L, Pan N, Qiu J, Zhang Z, Niu X, Yuan Z. Reproducibility of radiomics features derived from intravoxel incoherent motion diffusion-weighted MRI of cervical cancer. Acta Radiol 2021;62:679-86. [PMID: 32640886 DOI: 10.1177/0284185120934471] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
43 Kovalska M, Hnilicova P, Kalenska D, Tomascova A, Adamkov M, Lehotsky J. Effect of Methionine Diet on Time-Related Metabolic and Histopathological Changes of Rat Hippocampus in the Model of Global Brain Ischemia. Biomolecules 2020;10:E1128. [PMID: 32751764 DOI: 10.3390/biom10081128] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
44 van Houdt PJ, Saeed H, Thorwarth D, Fuller CD, Hall WA, McDonald BA, Shukla-Dave A, Kooreman ES, Philippens MEP, van Lier ALHMW, Keesman R, Mahmood F, Coolens C, Stanescu T, Wang J, Tyagi N, Wetscherek A, van der Heide UA. Integration of quantitative imaging biomarkers in clinical trials for MR-guided radiotherapy: Conceptual guidance for multicentre studies from the MR-Linac Consortium Imaging Biomarker Working Group. Eur J Cancer 2021;153:64-71. [PMID: 34144436 DOI: 10.1016/j.ejca.2021.04.041] [Reference Citation Analysis]
45 Li Q, Xiao Q, Li J, Wang Z, Wang H, Gu Y. Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer. Cancer Manag Res 2021;13:5053-62. [PMID: 34234550 DOI: 10.2147/CMAR.S304547] [Reference Citation Analysis]