Discovery Radiomics for Imaging-driven Quantitative Personalized Cancer Decision Support

Abstract

In this paper, we describe the underlying methodology behind discovery radiomics, where the ultimate goal is to discover customized, abstract radiomic feature models directly from the wealth of medical imaging data to better capture highly unique tumor traits beyond what can be captured using hand-crafted radiomic feature models. We further explore the current state-of-the-art in discovery radiomics and their application to various forms of cancer such as prostate cancer and lung cancer, and show that discovery radiomics can yield significant potential clinical impact.