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
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
Using multi-modal MRI data (FA, MD, T2w and SWI) from the CENTER-TBI study, SVMs were employed to predict patient outcome after mTBI. Z-scoring of image intensities was found beneficial resulting in a prediction accuracy of 67.7%.
Figure 1. Mean Z-scored FA and MD distribution across the discriminative voxels selected via RFE-SVM.
Figure 2. Selected voxels for outcome prediction. Color-coding according to the image modalities from which each voxel was selected. Radiological orientation.
