Artificial Intelligence (AI) shows a huge promise of analyzing huge sets of medical imaging and patterns that can be lost by human observers. The interpretation of brain scans supported by AI can help improve the care of children with brain tumors called gliomas, which are usually therapeutic but vary at risk of relapse. Researchers from mass general Brigham and his associates at Boston Children’s Hospital and Dana-Farber/Boston Children’s Cancer and Blood Disorders Center trained deep learning algorithms for the analysis of successive, post-therapeutic brain-scanning and cancer. Their results are published in The Journal of Medicine of New England.
Many pediatric gliomas are only therapeutic with surgery, but when relapses occur, they can be catastrophic. It is very difficult to predict who may be at risk of recurrence, so patients undergo frequent monitoring with magnetic resonance imaging (MR) for many years, a process that can be stressful and burdensome for children and families. We need better tools to detect early which patients are at the highest risk of relapse. ”
Benjamin Kann, MD, corresponding author of the Artificial Intelligence Program in Medicine (AIM) in Mass General Brigham and the Radiation oncology Department at Brigham and Women’s Hospital
Studies of relatively rare diseases, such as pediatric cancers, can be challenged by limited data. This study, which was partially funded by the National Institutes of Health, utilized institutional corporate relations across the country to collect about 4,000 mR scans from 715 pediatric patients. To maximize what AI could learn from a patient’s brain scans – and predict more accurately – the researchers used a technique called time learning, which trains the model to compose the findings from multiple brain scanning.
Usually, AI models for medical illustration are trained to draw conclusions from individual scans. With time learning, which has not been previously used for the medical depiction of the AI research, the images obtained over time inform the prediction of the algorithm of cancer recurrence. To develop the model of time learning, the researchers were training the model for the first time to follow the patient’s post-surgery scans in chronological order so that the model could learn to recognize subtle changes. From there, the researchers regulated the model to properly associate changes with the subsequent recurrence of cancer, where it is suitable.
Finally, the researchers found that the model of time learning predicted the recurrence of either low or high quality glioma from one year after treatment, with precision 75-89 %-better than accuracy associated with individual images-based predictions, which found it about 50 percent. The supply of AI with images from more post -treatment points increased the accuracy of the model’s prediction, but only four to six images are required before this improvement.
Researchers warn that further validation in additional arrangements is necessary before clinical application. In the end, they hope to start clinical trials to determine whether the risk predictions that have been informed by A can lead to improvements in care-whether by reducing the imaging frequency for patients with a lower risk or by preventive treatment of high-risk patients with targeted treatments.
“We have shown that AI is capable of efficiently analyzing and making predictions from multiple images, not just individual scans,” said first author Divyanshu Tak, MS, of the AIM program at Mass General Brigham and the Brigham oncology section. “This technique can be applied to many arrangements where patients get serial, longitudinal imaging and we are excited to see what this project will inspire.”
Source:
Magazine report:
Tak, D., et al. (2025) Predicting longitudinal risk for pediatric glioma with time deep learning. Nejm ai. doi.org/10.1056/aioa2400703.
