Reflections on radiogenomics and oncologic radiomics

Excerpt

With the rise of precision medicine and growing number of targeted treatment options for cancer, there has been dramatic growth in the amount and type of information we would like to extract from individual patients and tumors. Biomarkers in the laboratory have helped validate hypotheses across a number of different tumors; imaging data have been a key biomarker both for trial design and assessment of efficacy. Radiomics allows high-throughput extraction of quantitative features used to convert images into mineable data. This exploding field attempts to capture and quantify a wide variety of parameters in the image and translate these findings into distinct imaging phenotypes. Oncologic radiomics, as we are using it in this special issue, is a method for extracting imaging features that may be descriptive of tumor pathologic features, tumor behaviors, or clinical outcomes for a spectrum of tumor types. As stated by Elkarghali et al in their review in this issue, radiomics in general, is a method, while radiogenomics is a specific application of “oncologic radiomics” which uses imaging features to non-invasively identify or predict specific genomic alterations in the tumor (e.g., KRAS mutation status) that may impact tumor behavior or the way a tumor responds to treatment. Although technically not radiogenomics, these methods can be extrapolated into proteomics or metabolomics, as described by Scrima et al. and Shih et al. in this issue. Biopsy has been the mainstay of determining these features currently; however, it is expensive, invasive, and assesses only the sampled section of a heterogeneous tumor. Non-invasive imaging assessment of the whole tumor or of a multi-focal tumor burden in a single patient has the potential to provide a more comprehensive look at the tumor and disease in a non-invasive way. …