title: Regional prediction of tissue fate in acute ischemic stroke.
abstract: Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model of tissue fate that combines regional imaging features available after onset. A key component is the use of cuboids randomly sampled during the learning process. Models trained with time-to-maximum feature (Tmax) computed from perfusion weighted images (PWI) are compared to the ones obtained from the apparent diffusion coefficient (ADC). The prediction task is formalized as a regression problem where the inputs are the local cuboids extracted from Tmax or ADC images at onset, and the output is the segmented FLAIR intensity of the tissue 4 days after intervention. Experiments on 25 acute stroke patients demonstrate the effectiveness of the proposed approach in predicting tissue fate. Results on our dataset show the superiority of the regional model vs. a single-voxel-based approach, indicate that PWI regional models outperform ADC models, and demonstrates that a nonlinear regression model significantly improves the results in comparison to a linear model.
authors.affiliation: Department of Neurology & Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, USA. fabien.scalzo@gmail.com
authors.forename: Fabien
authors.initials: F
authors.lastname: Scalzo
authors.forename: Qing
authors.initials: Q
authors.lastname: Hao
authors.forename: Jeffry R
authors.initials: JR
authors.lastname: Alger
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
authors.forename: David S
authors.initials: DS
authors.lastname: Liebeskind
mesh_terms: D000208:Acute Disease; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000000981:diagnostic imaging / Q000503:physiopathology; D002545:Brain Ischemia / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000503:physiopathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D011379:Prognosis; D011859:Radiography; D020521:Stroke / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000503:physiopathology