Risk prediction is increasingly playing a key role in personalized cancer screening, prognostication and treatment. As cancer is a heterogeneous disease, with known inter-tumor and intra-tumor heterogeneity, established histopathologic prognostic biomarkers generally acquired from biopsy samples may be limited by sampling variation. Radiomics, and the broader field of Artificial Intelligence (AI) in radiologic image analysis, is an emerging field with the potential to provide novel markers of cancer risk, as well as leverage the whole tumor via non-invasive sampling to extract high throughput, quantitative features for the volumetric characterization of tumor heterogeneity. Recent studies have shown that imaging phenotypes can also augment genetic and genomic assays in precision screening, prognosis, and treatment. Identifying novel computational imaging biomarkers and integrating them with other emerging prognostic and predictive markers with data science approaches and AI to better predict patient outcomes has a potential to ultimately transform precision cancer care.
