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Abstract #0589

The role of predictive algorithm selection on the accuracy of MRI-based prediction of tissue outcome after acute ischemic stroke

Mark JRJ Bouts 1 , Elissa McIntosh 1 , Raquel Bezerra 1 , Izzuddin Diwan 1 , Steven Mocking 1 , Priya Garg 1 , William T Kimberly 2 , Ethem M Arsava 1 , William A Copen 3 , Pamela W Schaefer 3 , Hakan Ay 1 , Aneesh B Singhal 2 , Bruce R Rosen 1 , Rick M Dijkhuizen 4 , and Ona Wu 1

1 Athinoula A. Martinos Center, Dept Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States, 2 Dept Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States, 3 Dept Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States, 4 Biomedical imaging & Spectroscopy group, Image Sciences Institute, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

MRI-based prediction algorithms may more accurately assess tissue at risk of infarction following acute ischemic stroke than perfusion-diffusion mismatch. Yet, few studies quantitatively evaluated the predictive performance of several algorithms. We evaluated four algorithms in a cohort of acute ischemic stroke patients not receiving subsequent revascularization intervention nor novel therapeutics. Two regression models (generalized linear model, generalized additive model) were tested against two ensemble methods (adaptive boosting and random forest). All algorithms performed better than perfusion-diffusion mismatch for predicting follow-up infarct. More complex algorithms offered improved accuracy in predicting tissue infarction after stroke.

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