RT Journal Article
SR Electronic
T1 Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 208
OP 216
DO 10.3174/ajnr.A5391
VO 39
IS 2
A1 Zhou, M.
A1 Scott, J.
A1 Chaudhury, B.
A1 Hall, L.
A1 Goldgof, D.
A1 Yeom, K.W.
A1 Iv, M.
A1 Ou, Y.
A1 Kalpathy-Cramer, J.
A1 Napel, S.
A1 Gillies, R.
A1 Gevaert, O.
A1 Gatenby, R.
YR 2018
UL http://www.ajnr.org/content/39/2/208.abstract
AB 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 qualitative 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.LBPlocal binary patternsHOGhistogram of oriented gradientsQINQuantitative Imaging NetworkSIFTscale-invariant feature transform