Early identification of potentially salvageable tissue with MRI-based predictive algorithms after experimental ischemic stroke.

ABSTRACT: Individualized stroke treatment decisions can be improved by accurate identification of the extent of salvageable tissue. Magnetic resonance imaging (MRI)-based approaches, including measurement of a 'perfusion-diffusion mismatch' and calculation of infarction probability, allow assessment of tissue-at-risk; however, the ability to explicitly depict potentially salvageable tissue remains uncertain. In this study, five predictive algorithms (generalized linear model (GLM), generalized additive model, support vector machine, adaptive boosting, and random forest) were tested in their potency to depict acute cerebral ischemic tissue that can recover after reperfusion. Acute T2-, diffusion-, and perfusion-weighted MRI, and follow-up T2 maps were collected from rats subjected to right-sided midd

SUBMITTER: Bouts MJ 

PROVIDER: S-EPMC3705436 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature