Genomics models in radiotherapy: From mechanistic to machine learning
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 01, 2020
- Source ID
- 10.1002/mp.13751
Entities
People
- Barry S. Rosenstein
- James T. Coates
- John Kang
- Robert L. Strawderman
- Sarah L. Kerns
Organizations
- Icahn School of Medicine at Mount Sinai
- United States Department of Defense
- University of Oxford
- University of Rochester