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

M. Zhou, J. Scott, B. Chaudhury, L. Hall, D. Goldgof, K.W. Yeom, M. Iv, Y. Ou, J. Kalpathy-Cramer, S. Napel, R. Gillies, O. Gevaert (+1 others)
2017 American Journal of Neuroradiology  
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
more » ... haracteristics 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. ABBREVIATIONS: LBP ϭ local binary patterns; HOG ϭ histogram of oriented gradients; QIN ϭ Quantitative Imaging Network; SIFT ϭ scale-invariant feature transform C linical imaging captures enormous amounts of information, Indicates open access to non-subscribers at http://dx.
doi:10.3174/ajnr.a5391 pmid:28982791 pmcid:PMC5812810 fatcat:fovsdrilurg4xp7ghpjimk7pge