Genomics models in radiotherapy: From mechanistic to machine learning. Issue 5 (17th May 2020)

• Record Type: Journal Article
• Authors: Kang, John; Coates, James T.; Strawderman, Robert L.; Rosenstein, Barry S.; Kerns, Sarah L.
• Other Names: El Naqa Issam guestEditor.; Das Shiva K. guestEditor.
• Is Part Of: Medical physics. Volume 47:Issue 5(2020)
• Page Start: e203
• Page End: e217
• Publication Date: 2020-05-17
• Subjects: black box model -- modeling -- radiogenomics -- radiosensitivity; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13751
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Machine learning (ML) provides a broad framework for addressing high‐dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.