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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