Discovery Radiomics for Imaging-driven Quantitative Personalized Cancer Decision Support

Alexander Wong, Audrey G. Chung, Devinder Kumar, Mohammad Javad Shafiee, Farzad Khalvati, Masoom Haider


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.

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