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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 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.

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.

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