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 Pinto^1,2, Stefan Winzeck^3,4, Marta M. Correia⁵, Evgenios N. Kornaropoulos^4,6, 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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