Role of Machine Learning in Predicting Radiation-Induced Toxicity

Abstract

Background: Radiation therapy is fundamental to cancer treatment but frequently entails various levels of radiation-induced damage. Forecasting which people are most susceptible to substantial toxicity poses a problem owing to inter-individual heterogeneity. Machine learning (ML) methodologies provide a means to model intricate, nonlinear associations across clinical, dosimetric, and genetic factors, thereby facilitating enhanced toxicity prediction and individualized radiation planning.

Objective: To assess the efficacy of machine learning models in forecasting radiation-induced toxicity in cancer patients receiving radiotherapy at a tertiary care facility in India.

Methods: This prospective study was carried out at IGIMS, Patna, over a period of 24 months and involved 120 patients receiving radiation for diverse cancers. Clinical data, radiation dosimetry metrics, and patient-reported toxicity assessments were gathered. Multiple machine learning methods, including logistic regression, random forest, support vector machines (SVM), and gradient boosting, were trained and validated to forecast acute and late toxicity outcomes based on established toxicity grading (CTCAE v5.0). The evaluation of model performance was conducted utilizing metrics such as AUC, sensitivity, specificity, and F1-score.

Results: Among the 120 patients, radiation-induced toxicity was noted in 72 (60%). The random forest and gradient boosting models attained superior performance, with AUC values of 0.89 and 0.91, respectively. Key predictive factors encompassed total dosage, fractionation protocol, organ-specific dose-volume histogram (DVH) metrics, and pre-existing comorbidities. Machine learning models substantially surpassed conventional logistic regression in sensitivity and overall accuracy for toxicity prediction.

Conclusion: Machine learning methods demonstrate superior predictive accuracy for radiation-induced toxicity compared to conventional statistical models. Incorporating machine learning into radiation oncology workflows may enable the early detection of high-risk patients, assist with adaptive planning, and eventually improve treatment safety and personalization.

Keywords: Radiation-induced toxicity, machine learning, radiotherapy, predictive modeling, random forest, gradient boosting, CTCAE, dose-volume histogram, personalized oncology, IGIMS Patna