2024
DOI: 10.1101/2024.06.04.24308434
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Longitudinal risk prediction for pediatric glioma with temporal deep learning

Divyanshu Tak,
Biniam A. Garomsa,
Anna Zapaishchykova
et al.

Abstract: Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been… Show more

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