Longitudinal Risk Prediction for Pediatric Glioma with Temporal Deep Learning

Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence patterns and severity are heterogeneous and challenging to predict with established clinical and genomic markers. As a result, almost all children undergo frequent, long-term, magnetic resonance imaging (MRI) brain surveillance regardless of individual recurrence risk. Longitudinal deep-learning analysis of serial MRI scans may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers, but, thus far, progress has been limited by data availability and current machine-learning approaches.