Bioinformatics of Treatment Response

Abstract

Radiotherapy treatment outcomes are determined by complex interactions among treatment, anatomical, and patient-related variables. A key component of radiation oncology research is to predict at the time of treatment planning, or during the course of fractionated radiation treatment, the probability of tumor eradication and normal tissue risks for the type of treatment being considered for that particular patient. Traditionally, these outcomes are modeled using information about the dose distribution and the fractionation. However, it is recognized that radiation response is multifactorial including clinical prognostic factors and, more recently, inherited genetic variations have been suggested as playing an important role in radiation response. Therefore, recent approaches have utilized increasingly data-driven models incorporating advanced bioinformatics and machine learning tools in which dose-volume metrics are mixed with other patient-or disease-based prognostic factors in order to improve outcomes prediction. Accurate prediction of treatment outcomes would provide clinicians with better tools for informed decision-making about expected benefits versus anticipated risks. In this chapter, we provide an overview of the current status of data-driven outcome modeling techniques for patients who receive radiation treatment with special focus on its big data notion and the emerging role of machine learning approaches to improve outcome modeling and response prediction.