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Recent advancements in signal processing (SP) and machine learning, coupled with electronic medical record keeping in hospitals and the availability of extensive sets of medical images through internal/external communication systems, have resulted in a recent surge of interest in radiomics. Radiomics, an emerging and relatively new research field, refers to extracting semiquantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models. In the near future, it is expected to be a critical component for integrating image-derived information used for personalized treatment. The conventional radiomics workflow is typically based on extracting predesigned features (also referred to as handcrafted or engineered features) from a segmented region of interest (ROI). Nevertheless, recent advancements in deep learning have inspired trends toward deep-learning …