AI-assisted interpretation of brain scans may help improve care for children with brain tumors called gliomas, which are typically treatable but vary in risk of recurrence. Investigators from Mass General Brigham and collaborators at Boston Children’s Hospital and Dana-Farber/Boston Children’s Cancer and Blood Disorders Center trained deep learning algorithms to analyze sequential, post-treatment brain scans and flag patients at risk of cancer recurrence.

Many pediatric gliomas are curable with surgery alone, but when relapses occur, they can be devastating, said corresponding author Benjamin Kann, MD, of the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham and the Department of Radiation Oncology at Brigham and Women’s Hospital. It is very difficult to predict who may be at risk of recurrence, so patients undergo frequent follow-up with magnetic resonance (MR) imaging for many years, a process that can be stressful and burdensome for children and families. We need better tools to identify early which patients are at the highest risk of recurrence.

Studies of relatively rare diseases, like pediatric cancers, can be challenged by limited data. This study, which was funded in part by the National Institutes of Health, leveraged institutional partnerships across the country to collect nearly 4,000 MR scans from 715 pediatric patients. To maximize what AI could “learn” from a patient’s brain scans and more accurately predict recurrence the researchers employed a technique called temporal learning, which trains the model to synthesize findings from multiple brain scans taken over the course of several months post-surgery. 

Typically, AI models for medical imaging are trained to draw conclusions from single scans; with temporal learning, which has not previously been used for medical imaging AI research, images acquired over time inform the algorithm’s prediction of cancer recurrence. To develop the temporal learning model, the researchers first trained the model to sequence a patient’s post-surgery MR scans in chronological order so that the model could learn to recognize subtle changes. From there, the researchers fine-tuned the model to correctly associate changes with subsequent cancer recurrence, where appropriate. 

Source: https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/artificial-intelligence-predicts-pediatric-brain-cancer-relapse