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

James T Grist, Stephanie Withey, Christopher Bennett, Heather E L Rose, Lesley Macpherson, Adam Oates, Stephen Powell, Jan Novak, Laurence Abernethy, Barry Pizer, Simon Bailey, Steven C Clifford, Dipayan Mitra, Theodoros N Arvanitis, Dorothee P Auer, Shivaram Avula, Richard Grundy, Andrew C Peet

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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 diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.

Original languageEnglish
Article number18897
JournalScientific Reports
Issue number1
Publication statusPublished - 23 Sept 2021

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© 2021. The Author(s).

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