From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

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-based radiomics (DLRs) (also referred to as discovery radiomics). In addition to the advantages of these two approaches, there are also hybrid solutions that exploit the potential of multiple data sources. Considering the variety of approaches to radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This article provides a unique interdisciplinary perspective on radiomics by discussing state-of-the-art SP solutions in the context of radiomics.