Embedding machine learning based toxicity models within radiotherapy treatment plan optimization.

This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data.The study uses the optimization with constraint learning (OCL) framework, incorporating patient-specific factors into the optimization process.