Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

ABSTRACT: Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of

SUBMITTER: Grist JT 

PROVIDER: S-EPMC8460620 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature