Genomics models in radiotherapy: from mechanistic to machine learning

TL;DR

This paper reviews the development of radiogenomics modeling frameworks and machine learning applications in genomically-guided radiotherapy, highlighting advances in predictive models for radiosensitivity and clinical biomarkers.

Contribution

It provides a comprehensive overview of current radiogenomics modeling approaches and the evolution of machine learning techniques in radiation biology and therapy.

Findings

Overview of radiogenomics modeling frameworks

Discussion of machine learning evolution in radiotherapy

Summary of efforts in developing predictive biomarkers

Abstract

Machine learning provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While machine learning is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data towards questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts towards 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 machine learning to create predictive models for radiogenomics.