In the 1980s and 1990s, quantitative imaging scientists and engineers were developing algorithms for the extraction of imaging phenotypes from radiographic images for use in computer-aided detection/diagnosis and for risk assessment and prognostic/predictive tasks.6,7 However, it wasn’t until the early part of the century when researchers began exploring links between the imaging and molecular phenotypes. For example, in 2002, Huo et al. showed the relationship between computerized texture analysis of the breast parenchyma on mammography and presence of the BRAC1/BRCA2 gene mutation.8 In 2007, Segal et al. reported that radiological observations of tumors seen on CT “systematically correlate with the global gene expression programs of primary human liver cancer” derived using microarray analysis of the resected tumor.9 In 2008, Diehn et al. reported linkages between the imaging phenotype of glioblastoma multiforme (GBM) on MRI to the molecular phenotype derived using DNA microarray analysis10 and survival. And in 2010, Bhooshan et al. demonstrated relationships between computer-extracted MRI phenotypes and breast cancer subtype and aggressiveness.11 Many papers have since expanded the literature on deriving quantitative image features, deriving and reducing the interobserver variability of semantic image features, associating image features with molecular phenotypes, genetics, and outcomes, and the results of mining these associations for discovery (e.g., see Refs. 121314151617–18).