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Recent advancements in signal processing and machine learning coupled withdevelopments of electronic medical record keeping in hospitals and theavailability of extensive set of medical images through internal/externalcommunication systems, have resulted in a recent surge of significant interestin "Radiomics". Radiomics is an emerging and relatively new research field,which refers to extracting semi-quantitative and/or quantitative features frommedical images with the goal of developing predictive and/or prognostic models,and is expected to become a critical component for integration of image-derivedinformation for personalized treatment in the near future. The conventionalRadiomics workflow is typically based on extracting pre-designed features (alsoreferred to as hand-crafted or engineered features) from a segmented region ofinterest. Nevertheless, recent advancements in deep learning have caused trendstowards deep learning-based Radiomics (also referred to as discoveryRadiomics). Considering the advantages of these two approaches, there are alsohybrid solutions developed to exploit the potentials of multiple data sources.Considering the variety of approaches to Radiomics, further improvementsrequire a comprehensive and integrated sketch, which is the goal of thisarticle. This manuscript provides a unique interdisciplinary perspective onRadiomics by discussing state-of-the-art signal processing solutions in thecontext of Radiomics.

From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities