ABSTRACT

Radiogenomics, is a burgeoning area of research that aims to link medical imaging with multi-omics molecular profiles of the same patients. Radiogenomics has shown its potential through its ability to predict clinical outcomes e.g. prognosis, and through predicting actionable molecular properties of tumors, e.g. the activity of EGFR, a major drug target in many cancers. Combining these complementary data sources in a radiogenomics framework for data fusion can have profound contributions toward predicting treatment outcomes by uncovering unknown synergies and relationships. More specifically, developing computational models integrating quantitative image features and molecular data to develop radiogenomics signatures, holds the potential to translate in benefit to tumor patients by investigating biomarkers that accurately predict therapy response of tumors. Readily, because medical imaging is part of the routine diagnostic work-up of cancer patients and molecular data of human tumors is increasingly being used in clinical workflows, therefore if reliable radiogenomic signatures can be found reflecting treatment response, translation to the clinical applications is feasible. In this chapter, we discuss the rationale for radiogenomics, and the key components that make up a radiogenomic model.