Abstract
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.
Highlights
Additional studies indicated that quantitative MR imaging features derived from entire tumor volumes can be used to identify glioblastoma subtypes with distinct molecular pathway activities.[15,46]
The value of such whole-tumor comparisons is limited by the spatial variations in both the imaging features and molecular tumor properties of tumor cells in, for example, glioblastoma multiforme
The rise of deep learning as a new frontier in machine learning has advanced large-scale medical image analysis.[48]. We describe these learning strategies and highlight specific clinical applications in the context of brain tumors
Summary
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches.
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