Longitudinal Risk Prediction for Pediatric Glioma with Temporal Deep Learning

BACKGROUND: 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. METHODS: We developed a self-supervised temporal deep-learning approach tailored for longitudinal medical imaging analysis, wherein a multistep model encodes patients' serial MRI scans and is trained to classify the correct chronological order as a pretext task. The pretrained model is then fine-tuned to predict the primary end point of interest - in this case, 1-year recurrence prediction for pediatric gliomas from the point of last scan - by leveraging a patient's historical postoperative surveillance scans. We apply the model across 3994 scans from 715 patients followed at three separate institutions in the setting of pediatric low- and high-grade gliomas. RESULTS: Longitudinal imaging analysis with temporal learning improved recurrence prediction performance (F1 score) by up to 58.5% (range, 6.6 to 58.5%) compared with traditional approaches across datasets, with performance improvements in both low- and high-grade gliomas and area under the receiver operating characteristic curve of (range, 75 to 89%) across all datasets. Recurrence prediction performance increased incrementally with the number of historical scans available per patient, reaching plateaus between three and six scans, depending on the dataset. CONCLUSIONS: Temporal deep learning enables high-performing longitudinal medical imaging analysis and point-of-care decision support for pediatric brain tumors. Temporal learning may be broadly adaptable to track and predict risk in patients with other cancers and chronic diseases undergoing surveillance imaging. (Funded in part by the National Institutes of Health/National Cancer Institute (U54 CA274516 and P50 CA165962), and Botha-Chan Low Grade Glioma Consortium.).