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Abstract #0375

The deep topology of glioma

James K Ruffle1, Samia Mohinta1, Robert Gray1, Chris Foulon1, Sebastian Brandner2, Harpreet Hyare1, and Parashkev Nachev1
1High-Dimensional Neurology, UCL Queen Square Institute of Neurology, London, United Kingdom, 2Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence

Motivation: Heterogeneity in neuro-oncology is a major barrier to clinical and academic innovation, resolvable only with expressive computational modelling, large-scale data, and high-performance computing.

Goal(s): To develop a comprehensive mathematical modelling framework illuminative to glioma's multi-modal heterogeneity.

Approach: In the largest and most detailed international investigation of glioma patients (n=4908 across six different cohort sites), we undertake cartography to characterise the interplay between lesion development and the space they occupy.

Results: We reveal the spatial properties of glioma, a deep auto-encoder representation, and from which we develop machine models predicting patient-personalised diagnosis and prognosis, disclosing the joint relationship between imaging appearances and underlying neuropathology.

Impact: These works illustrate the benefit of computational modelling across clinical neuro-oncological imaging data in patient-personalised care, including diagnostic and outcome prediction, paving the way for future research and clinical translation.

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Keywords