BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Larroza A, Moratal D, Paredes-Sánchez A, Soria-Olivas E, Chust ML, Arribas LA, Arana E. Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. J Magn Reson Imaging 2015;42:1362-8. [PMID: 25865833 DOI: 10.1002/jmri.24913] [Cited by in Crossref: 55] [Cited by in F6Publishing: 42] [Article Influence: 7.9] [Reference Citation Analysis]
Number Citing Articles
1 Nakagawa M, Nakaura T, Namimoto T, Kitajima M, Uetani H, Tateishi M, Oda S, Utsunomiya D, Makino K, Nakamura H, Mukasa A, Hirai T, Yamashita Y. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma. European Journal of Radiology 2018;108:147-54. [DOI: 10.1016/j.ejrad.2018.09.017] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 4.0] [Reference Citation Analysis]
2 Chen X, Parekh VS, Peng L, Chan MD, Redmond KJ, Soike M, McTyre E, Lin D, Jacobs MA, Kleinberg LR. Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery. Neurooncol Adv 2021;3:vdab150. [PMID: 34901857 DOI: 10.1093/noajnl/vdab150] [Reference Citation Analysis]
3 Béresová M, Larroza A, Arana E, Varga J, Balkay L, Moratal D. 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. Magn Reson Mater Phy 2018;31:285-94. [DOI: 10.1007/s10334-017-0653-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis]
4 Tong E, McCullagh KL, Iv M. Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response. Front Neurol 2020;11:270. [PMID: 32351445 DOI: 10.3389/fneur.2020.00270] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
5 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]
6 Yan PF, Yan L, Hu TT, Xiao DD, Zhang Z, Zhao HY, Feng J. The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation. Transl Oncol 2017;10:570-7. [PMID: 28654820 DOI: 10.1016/j.tranon.2017.04.006] [Cited by in Crossref: 39] [Cited by in F6Publishing: 35] [Article Influence: 7.8] [Reference Citation Analysis]
7 Nardone V, Tini P, Biondi M, Sebaste L, Vanzi E, De Otto G, Rubino G, Carfagno T, Battaglia G, Pastina P, Cerase A, Mazzoni LN, Banci Buonamici F, Pirtoli L. Prognostic Value of MR Imaging Texture Analysis in Brain Non-Small Cell Lung Cancer Oligo-Metastases Undergoing Stereotactic Irradiation. Cureus 2016;8:e584. [PMID: 27226944 DOI: 10.7759/cureus.584] [Cited by in Crossref: 3] [Cited by in F6Publishing: 8] [Article Influence: 0.5] [Reference Citation Analysis]
8 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]
9 López-Gómez C, Ortiz-Ramón R, Mollá-Olmos E, Moratal D; Alzheimer’s Disease Neuroimaging Initiative. ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer's Disease by Means of Textures Analysis on Magnetic Resonance Images. Diagnostics (Basel) 2018;8:E47. [PMID: 30029524 DOI: 10.3390/diagnostics8030047] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]
10 Zhang Z, Sejdić E. Radiological images and machine learning: Trends, perspectives, and prospects. Comput Biol Med 2019;108:354-70. [PMID: 31054502 DOI: 10.1016/j.compbiomed.2019.02.017] [Cited by in Crossref: 38] [Cited by in F6Publishing: 21] [Article Influence: 12.7] [Reference Citation Analysis]
11 Zhang Z, Yang J, Ho A, Jiang W, Logan J, Wang X, Brown PD, McGovern SL, Guha-Thakurta N, Ferguson SD, Fave X, Zhang L, Mackin D, Court LE, Li J. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 2018;28:2255-63. [PMID: 29178031 DOI: 10.1007/s00330-017-5154-8] [Cited by in Crossref: 48] [Cited by in F6Publishing: 46] [Article Influence: 9.6] [Reference Citation Analysis]
12 Shi Y, Lu X, Zhang L, Shu H, Gu L, Wang Z, Gao L, Zhu J, Zhang H, Zhou D, Zhang Z. Potential Value of Plasma Amyloid-β, Total Tau, and Neurofilament Light for Identification of Early Alzheimer's Disease. ACS Chem Neurosci 2019;10:3479-85. [PMID: 31145586 DOI: 10.1021/acschemneuro.9b00095] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 6.0] [Reference Citation Analysis]
13 Wang H, Xue J, Qu T, Bernstein K, Chen T, Barbee D, Silverman JS, Kondziolka D. Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps. Med Phys 2021. [PMID: 34287940 DOI: 10.1002/mp.15110] [Reference Citation Analysis]
14 Larroza A, López-Lereu MP, Monmeneu JV, Gavara J, Chorro FJ, Bodí V, Moratal D. Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction. Med Phys 2018;45:1471-80. [PMID: 29389013 DOI: 10.1002/mp.12783] [Cited by in Crossref: 26] [Cited by in F6Publishing: 21] [Article Influence: 6.5] [Reference Citation Analysis]
15 Kitz J, Lowes LE, Goodale D, Allan AL. Circulating Tumor Cell Analysis in Preclinical Mouse Models of Metastasis. Diagnostics (Basel) 2018;8:E30. [PMID: 29710776 DOI: 10.3390/diagnostics8020030] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 3.5] [Reference Citation Analysis]
16 Zhou W, Zhang L, Wang K, Chen S, Wang G, Liu Z, Liang C. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging. 2017;45:1476-1484. [PMID: 27626270 DOI: 10.1002/jmri.25454] [Cited by in Crossref: 63] [Cited by in F6Publishing: 51] [Article Influence: 10.5] [Reference Citation Analysis]
17 Prasanna P, Tiwari P, Madabhushi A. Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor. Sci Rep 2016;6:37241. [PMID: 27872484 DOI: 10.1038/srep37241] [Cited by in Crossref: 60] [Cited by in F6Publishing: 51] [Article Influence: 10.0] [Reference Citation Analysis]
18 Bian H, Wang X, Yu Y, Wu X, Chen D, Gao J. The identification of blood species using the correlation coefficient of sub-spectra based on Raman spectroscopy. Optik 2020;200:163312. [DOI: 10.1016/j.ijleo.2019.163312] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Guan Y, Li W, Jiang Z, Zhang B, Chen Y, Huang X, Zhang J, Liu S, He J, Zhou Z, Ge Y. Value of whole-lesion apparent diffusion coefficient (ADC) first-order statistics and texture features in clinical staging of cervical cancers. Clinical Radiology 2017;72:951-8. [DOI: 10.1016/j.crad.2017.06.115] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 2.2] [Reference Citation Analysis]
20 Kim HY, Cho SJ, Sunwoo L, Baik SH, Bae YJ, Choi BS, Jung C, Kim JH. Classification of true progression after radiotherapy of brain metastasis on MRI using artificial intelligence: a systematic review and meta-analysis. Neurooncol Adv 2021;3:vdab080. [PMID: 34377988 DOI: 10.1093/noajnl/vdab080] [Reference Citation Analysis]
21 Tian Y, Komolafe TE, Zheng J, Zhou G, Chen T, Zhou B, Yang X. Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features. Diagnostics (Basel) 2021;11:1875. [PMID: 34679573 DOI: 10.3390/diagnostics11101875] [Reference Citation Analysis]
22 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]
23 Molina D, Pérez-Beteta J, Martínez-González A, Martino J, Velasquez C, Arana E, Pérez-García VM. Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization. PLoS One 2017;12:e0178843. [PMID: 28586353 DOI: 10.1371/journal.pone.0178843] [Cited by in Crossref: 29] [Cited by in F6Publishing: 28] [Article Influence: 5.8] [Reference Citation Analysis]
24 Rui W, Ren Y, Wang Y, Gao X, Xu X, Yao Z. MR textural analysis on T 2 FLAIR images for the prediction of true oligodendroglioma by the 2016 WHO genetic classification: Prediction of True Oligodendroglioma. J Magn Reson Imaging 2018;48:74-83. [DOI: 10.1002/jmri.25896] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 1.8] [Reference Citation Analysis]
25 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]
26 Freiman M, Manjeshwar R, Goshen L. Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders. Med Phys 2019;46:2223-31. [PMID: 30821364 DOI: 10.1002/mp.13464] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
27 Salvestrini V, Greco C, Guerini AE, Longo S, Nardone V, Boldrini L, Desideri I, De Felice F. The role of feature-based radiomics for predicting response and radiation injury after stereotactic radiation therapy for brain metastases: A critical review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). Transl Oncol 2022;15:101275. [PMID: 34800918 DOI: 10.1016/j.tranon.2021.101275] [Reference Citation Analysis]
28 Chakraborty S, Aich S, Kim HC. 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson's Disease Using Artificial Neural Networks. Healthcare (Basel) 2020;8:E34. [PMID: 32046073 DOI: 10.3390/healthcare8010034] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
29 Thust SC, van den Bent MJ, Smits M. Pseudoprogression of brain tumors. J Magn Reson Imaging 2018. [PMID: 29734497 DOI: 10.1002/jmri.26171] [Cited by in Crossref: 83] [Cited by in F6Publishing: 67] [Article Influence: 20.8] [Reference Citation Analysis]
30 Nakagawa M, Nakaura T, Namimoto T, Iyama Y, Kidoh M, Hirata K, Nagayama Y, Yuki H, Oda S, Utsunomiya D, Yamashita Y. Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features. Acad Radiol 2019;26:1390-9. [PMID: 30661978 DOI: 10.1016/j.acra.2018.11.014] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
31 Sun JW, Fan R, Wang Q, Wang QQ, Jia XZ, Ma HB. Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification. Brain Res 2021;1757:147299. [PMID: 33516816 DOI: 10.1016/j.brainres.2021.147299] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Zhang YD, Wang J, Wu CJ, Bao ML, Li H, Wang XN, Tao J, Shi HB. An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification. Oncotarget 2016;7:78140-51. [PMID: 27542201 DOI: 10.18632/oncotarget.11293] [Cited by in Crossref: 24] [Cited by in F6Publishing: 20] [Article Influence: 6.0] [Reference Citation Analysis]
33 Tiwari P, Prasanna P, Wolansky L, Pinho M, Cohen M, Nayate AP, Gupta A, Singh G, Hatanpaa KJ, Sloan A, Rogers L, Madabhushi A. Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study. AJNR Am J Neuroradiol 2016;37:2231-6. [PMID: 27633806 DOI: 10.3174/ajnr.A4931] [Cited by in Crossref: 59] [Cited by in F6Publishing: 30] [Article Influence: 9.8] [Reference Citation Analysis]
34 Prasanna P, Rogers L, Lam TC, Cohen M, Siddalingappa A, Wolansky L, Pinho M, Gupta A, Hatanpaa KJ, Madabhushi A, Tiwari P. Disorder in Pixel-Level Edge Directions on T1WI Is Associated with the Degree of Radiation Necrosis in Primary and Metastatic Brain Tumors: Preliminary Findings. AJNR Am J Neuroradiol 2019;40:412-7. [PMID: 30733252 DOI: 10.3174/ajnr.A5958] [Cited by in Crossref: 2] [Cited by in F6Publishing: 6] [Article Influence: 0.7] [Reference Citation Analysis]
35 Bressem KK, Adams LC, Vahldiek JL, Erxleben C, Poch F, Lehmann KS, Hamm B, Niehues SM. Subregion Radiomics Analysis to Display Necrosis After Hepatic Microwave Ablation-A Proof of Concept Study. Invest Radiol. 2020;55:422-429. [PMID: 32028297 DOI: 10.1097/rli.0000000000000653] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
36 Peng L, Parekh V, Huang P, Lin DD, Sheikh K, Baker B, Kirschbaum T, Silvestri F, Son J, Robinson A, Huang E, Ames H, Grimm J, Chen L, Shen C, Soike M, McTyre E, Redmond K, Lim M, Lee J, Jacobs MA, Kleinberg L. Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics. Int J Radiat Oncol Biol Phys 2018;102:1236-43. [PMID: 30353872 DOI: 10.1016/j.ijrobp.2018.05.041] [Cited by in Crossref: 52] [Cited by in F6Publishing: 44] [Article Influence: 13.0] [Reference Citation Analysis]
37 Lohmann P, Kocher M, Ceccon G, Bauer EK, Stoffels G, Viswanathan S, Ruge MI, Neumaier B, Shah NJ, Fink GR, Langen KJ, Galldiks N. Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. Neuroimage Clin 2018;20:537-42. [PMID: 30175040 DOI: 10.1016/j.nicl.2018.08.024] [Cited by in Crossref: 60] [Cited by in F6Publishing: 50] [Article Influence: 15.0] [Reference Citation Analysis]
38 Loizou CP, Pantzaris M, Pattichis CS. Normal appearing brain white matter changes in relapsing multiple sclerosis: Texture image and classification analysis in serial MRI scans. Magn Reson Imaging 2020;73:192-202. [PMID: 32890673 DOI: 10.1016/j.mri.2020.08.022] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
39 Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69:127-157. [PMID: 30720861 DOI: 10.3322/caac.21552] [Cited by in Crossref: 132] [Cited by in F6Publishing: 185] [Article Influence: 44.0] [Reference Citation Analysis]
40 Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, Keek SA, Sanduleanu S, Primakov SP, Beuque MPL, Marcus D, van der Wiel AMA, Zerka F, Oberije CJG, van Timmeren JE, Woodruff HC, Lambin P. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020;93:20190948. [PMID: 32101448 DOI: 10.1259/bjr.20190948] [Cited by in Crossref: 32] [Cited by in F6Publishing: 28] [Article Influence: 16.0] [Reference Citation Analysis]
41 Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 2017;27:4082-90. [PMID: 28374077 DOI: 10.1007/s00330-017-4800-5] [Cited by in Crossref: 111] [Cited by in F6Publishing: 97] [Article Influence: 22.2] [Reference Citation Analysis]
42 Akakuru OU, Iqbal MZ, Saeed M, Liu C, Paunesku T, Woloschak G, Hosmane NS, Wu A. The Transition from Metal-Based to Metal-Free Contrast Agents for T1 Magnetic Resonance Imaging Enhancement. Bioconjug Chem 2019;30:2264-86. [PMID: 31380621 DOI: 10.1021/acs.bioconjchem.9b00499] [Cited by in Crossref: 22] [Cited by in F6Publishing: 18] [Article Influence: 7.3] [Reference Citation Analysis]
43 Hwang EJ, Jung JY, Lee SK, Lee SE, Jee WH. Machine Learning for Diagnosis of Hematologic Diseases in Magnetic Resonance Imaging of Lumbar Spines. Sci Rep 2019;9:6046. [PMID: 30988360 DOI: 10.1038/s41598-019-42579-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
44 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]