Age-cognizant, fully-automated, high-dimensional modelling of brain tumour appearances on MR imaging

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Summary: Brain tumours remain the most common cause of cancer death in the US paediatric population and gliomas the second leading cause of cancer mortality in adults under 35. The rich diversity of tumour characteristics among glioma patients, even within the same tumour grade, is a big challenge for disease outcome prediction. Age-specific genetic differences are thought to account for the various clinical differences observed in malignant gliomas and the recent WHO reclassification of brain tumours incorporates, for the first time, molecular markers in addition to histopathology. This provides an opportunity to identify a complex "imaging fingerprint" that could not only be useful for non-invasive genotyping but also to model age-specific tumour growth rates, predict tumour transformation and brain invasion. The aim of this study is to integrate multimodal data in neuro-oncology, combining imaging, clinical, pathological and genetic variables to develop age-cognizant, fully-automated, high-dimensional modelling of brain tumours for the purpose of individual-level clinical triage, diagnosis, prognosis, and therapeutic inference.