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Background Brain tumours represent the highest cause of mortality in thepaediatric oncological population. Diagnosis is commonly performed withmagnetic resonance imaging and spectroscopy. Survival biomarkers arechallenging to identify due to the relatively low numbers of individual tumourtypes, especially for rare tumour types such as atypical rhabdoid tumours.  Methods 69 children with biopsy-confirmed brain tumours were recruited intothis study. All participants had both perfusion and diffusion weighted imagingperformed at diagnosis. Data were processed using conventional methods, and aBayesian survival analysis performed. Unsupervised and supervised machinelearning were performed with the survival features, to determine novelsub-groups related to survival. Sub-group analysis was undertaken to understanddifferences in imaging features, which pertain to survival.  Findings Survival analysis showed that a combination of diffusion andperfusion imaging were able to determine two novel sub-groups of brain tumourswith different survival characteristics (p <0.01), which were subsequentlyclassified with high accuracy (98%) by a neural network. Further analysis ofhigh-grade tumours showed a marked difference in survival (p=0.029) between thetwo clusters with high risk and low risk imaging features.  Interpretation This study has developed a novel model of survival forpaediatric brain tumours, with an implementation ready for integration intoclinical practice. Results show that tumour perfusion plays a key role indetermining survival in brain tumours and should be considered as a highpriority for future imaging protocols.

Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours