Artificial Intelligence Accurately Predicts Radiation Treatment Side Effects

This precision oncology approach has the potential to better identify patients who might benefit from early interventions that could help prevent significant weight loss after treatment or reduce the need for feeding tube placement.

The team of researchers developed models to analyze large sets of data merged from three sources: electronic health records (Epic), an internal web-based charting tool (Brocade) and the record/verify system (Mosaiq). The data included more than 700 clinical and treatment variables for patients with head and neck cancer (75% male/25% female, with a median age of 62 years) who received more than 2,000 courses of radiation therapy (median dose 60 Gy) across five practice sites at MD Anderson from 2016 to 2018.

The researchers used the models to predict three endpoints: significant weight loss, feeding tube placement and unplanned hospitalizations. Results from the best-performing model were then validated against 225 subsequent consecutive radiation therapy treatments. Models with a performance rate that met a pre-specified threshold of area under the curve (AUC) of 0.70 or higher were considered clinically valid (an AUC score of 1.0 would mean the model’s predictions were 100% accurate, while a score of 0.0 would mean the predictions were never accurate). The models predicted the likelihood of significant weight loss (AUC = 0.751) and need for feeding tube placement (AUC = 0.755) with a high degree of accuracy.

“Being able to identify which patients are at greatest risk would allow radiation oncologists to take steps to prevent or mitigate these possible side effects,” said Jay Reddy, MD, PhD, an assistant professor of radiation oncology at The University of Texas MD Anderson Cancer Center and lead author on the study. “If the patient has an intermediate risk, and they might get through treatment without needing a feeding tube, we could take precautions such as setting them up with a nutritionist and providing them with nutritional supplements. If we know their risk for feeding tube placement is extremely high – a better than 50% chance they would need one – we could place it ahead of time so they wouldn’t have to be admitted to the hospital after treatment. We’d know to keep a closer eye on that patient.”

The machine learning approach cannot isolate the single-most predictive factor or combination of factors that lead to negative side effects, but can provide patients and their clinicians with a better understanding of what to expect during the course of treatment. In addition to predicting the likelihood of side effects, machine learning models could potentially predict which treatment plans would be most effective for different types of patients and allow for more personalized approaches to radiation oncology.

“Machine learning can make doctors more efficient and treatment safer by reducing the risk of error,” added Dr. Reddy. “It has the potential for influencing all aspects of radiation oncology today – anything where a computer can look at data and recognize a pattern.”

Related Links:
University of Texas MD Anderson Cancer Center