MD Anderson researchers have developed the first machine learning algorithm to predict acute toxicities in patients receiving radiation therapy for head and neck cancers. The results of the study were presented today at the 61st Annual Meeting of the American Society for Radiation Oncology (ASTRO). "With head and neck radiation, a lot of toxicity occurs, however it's not always clear which patients will experience serious side effects," says study lead Jay Reddy, M.D., Ph.D., assistant professor of Radiation Oncology. Reddy's team set out to develop algorithms that could predict significant weight loss ( 10% during radiation therapy), feeding tube placement and unplanned hospitalizations with three months of beginning radiation treatment. "It's virtually unheard of for these patients to not lose any weight at all, but many patients are able to complete treatment without a feeding tube. Thus, we don't want to unnecessarily place one on a hunch. Prolonged time with a feeding tube can hamper efforts to rehabilitate swallowing muscles. "The challenge is to balance this concern with the knowledge that some patients can't get through treatment without assistance, and their need for nutrition becomes dire.