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Abstract #3256

Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study

Maira Siqueira Pinto1,2, Stefan Winzeck3,4, Marta M. Correia5, Evgenios N. Kornaropoulos4,6, David K. Menon4, Ben Glocker3, Arnold J. den Dekker2, Jan Sijbers2, Pieter-Jan Guns7, Pieter Van Dyck1, and Virginia F. J. Newcombe4
1Radiology, UZA - Antwerp University Hospital, Antwerpen, Belgium, 2imec-Vision Lab, University of Antwerp, Antwerpen, Belgium, 3BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom, 4Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 5MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, 6Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden, 7Physiopharmacology, University of Antwerp, Antwerpen, Belgium

Over 40% of patients after a mild traumatic brain injury (mTBI) may have persisting symptoms. This study investigated a Support Vector Machine (SVM) approach for outcome prediction after mTBI from multi-modal MRI. The datasets included 77 mTBI patients from the CENTER-TBI study with acute T2w, SWI, FA and MD scans and outcome scores six month post-injury. Benefits of data harmonization were tested and Z-scoring reduced site-specific biases yielding 67.7% prediction accuracy. Our data-driven approach revealed that predictive signal was retrieved mainly from diffusion maps rather than conventional images, and was located in the superior fronto-occipital fascicle and the corticospinal tract.

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