Machine Learning for the Prediction of Organ Damage from Cancer Radiotherapy

Abstract

There are over one million cases of cancer diagnosed each year just in the United States, and many times that number in other countries. About 60% of US cancer patients are treated with radiotherapy, and increasingly complex radiation delivery procedures are being developed in order to improve treatment outcomes. A key goal is to determine appropriate values for a large set of delivery parameters in order to ensure that as large a fraction as possible of the radiation that enters the patient is delivered to the tumor as opposed to depositing it in adjacent non-cancerous organs that can be damaged by radiation (the latter are termed organs-at-risk (OARs)). In the research described here, we address the issue of tissue damage by developing machine learning (ML) frameworks for the prediction of two types of possible OAR complications associated with an advanced form of treatment known as Intensity Modulated Radiation Therapy. Specifically, we show that ML techniques may be used to develop models for the prediction of radiation-induced (1) xerostomia (“dry mouth”, a significant quality-of-life concern associated with radiation damage to the parotid glands) and (2) rectal bleeding. These patient-specific ML models provide accurate complication prediction on the basis of the selected clinician settings for the radiation constraints. This research will thus allow clinicians to determine treatment parameter values that result in appropriate radiation levels for critical organs.