AI may help tailor treatment for childhood brain tumors
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Choosing the right treatment for a child with a malignant brain tumor requires a careful balance. Therapy must be strong enough to keep the cancer from coming back, but it can also cause lasting side effects that affect learning, development and quality of life. Now, an international team led by researchers at Dana-Farber Cancer Institute has developed an artificial intelligence model that may help doctors make those decisions with greater precision.
The findings are published in Nature Genetics.
Survival predictions tailored to each tumor
The study describes MANTIS (Methylation profiling and Artificial Neural networks for Time-dependent Individualized Survival predictions), an AI model that uses DNA methylation profiling, a test already used to help diagnose brain tumors, to generate personalized survival predictions for children with medulloblastoma and ependymoma.
The researchers found that MANTIS outperformed current risk-prediction approaches for many patients across several large validation cohorts. Rather than assigning patients to broad risk groups, the model estimates how survival probability changes over time based on the unique biology of each child's tumor.
Lower radiation doses for some children
The study also points to a potential path for reducing treatment intensity in some children with medulloblastoma. When the researchers adapted MANTIS to estimate the likely effects of different radiation doses, the model suggested that more than half of children with average-risk medulloblastoma may be candidates for lower-dose craniospinal irradiation. In the data analyzed, lower-dose radiation was associated with better cognitive outcomes, and the researchers did not detect a significant survival disadvantage in this group.
"Our goal is to move beyond broad risk categories and toward a more individualized understanding of each child's tumor," said Volker Hovestadt, Ph.D., a computational biologist at Dana-Farber and senior author of the study. "For children with brain tumors, that could help us better match treatment intensity to the biology of the disease and, ultimately, reduce long-term side effects for some patients."
Building on routine tumor testing
Current methods for predicting risk combine clinical and molecular features to help guide treatment. But children within the same risk group can still have very different outcomes. MANTIS was designed to capture that variation by combining genome-wide molecular data, including DNA methylation patterns and copy number changes, with long-term patient follow-up.
One of the strengths of the approach is that it builds on information that is already being generated as part of routine tumor testing. That could make it easier, over time, to extract more clinical value from a diagnostic assay many centers already use.
"One exciting aspect of this work is that it shows how artificial intelligence can learn from molecular tests that are already part of care," said Sabina Stefan, Ph.D., a postdoctoral researcher at Dana-Farber and first author of the study. "With further clinical study, this type of approach could help support more personalized treatment decisions not only for pediatric brain tumors but potentially for many other cancers as well."
The researchers first developed the model for medulloblastoma, one of the most common malignant brain tumors in children, and then extended the same framework to ependymoma, another pediatric brain tumor in which outcomes can vary widely. The findings suggest that similar AI-based methods could eventually be applied to other pediatric and adult cancers in which patients with the same diagnosis can have very different clinical courses.
Publication details
Sabina Stefan et al., AI-based precision prognostics and therapy personalization for childhood brain tumors, Nature Genetics (2026). DOI: 10.1038/s41588-026-02815-4
Journal information: Nature Genetics
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