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Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and... REVIEWARTICLE Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches X M. Zhou, X J. Scott, X B. Chaudhury, X L. Hall, X D. Goldgof, X K.W. Yeom, X M. Iv, X Y. Ou, X J. Kalpathy-Cramer, X S. Napel, X R. Gillies, X O. Gevaert, and X R. Gatenby ABSTRACT SUMMARY: Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and quali- tative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies. ABBREVIATIONS: LBP http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Neuroradiology American Journal of Neuroradiology

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

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References (82)

Publisher
American Journal of Neuroradiology
Copyright
© 2018 by American Journal of Neuroradiology
ISSN
0195-6108
eISSN
1936-959X
DOI
10.3174/ajnr.A5391
Publisher site
See Article on Publisher Site

Abstract

REVIEWARTICLE Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches X M. Zhou, X J. Scott, X B. Chaudhury, X L. Hall, X D. Goldgof, X K.W. Yeom, X M. Iv, X Y. Ou, X J. Kalpathy-Cramer, X S. Napel, X R. Gillies, X O. Gevaert, and X R. Gatenby ABSTRACT SUMMARY: Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and quali- tative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies. ABBREVIATIONS: LBP

Journal

American Journal of NeuroradiologyAmerican Journal of Neuroradiology

Published: Feb 1, 2018

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