title: Multi-center prediction of hemorrhagic transformation in acute ischemic stroke using permeability imaging features.
abstract: Permeability images derived from magnetic resonance (MR) perfusion images are sensitive to blood-brain barrier derangement of the brain tissue and have been shown to correlate with subsequent development of hemorrhagic transformation (HT) in acute ischemic stroke. This paper presents a multi-center retrospective study that evaluates the predictive power in terms of HT of six permeability MRI measures including contrast slope (CS), final contrast (FC), maximum peak bolus concentration (MPB), peak bolus area (PB), relative recirculation (rR), and percentage recovery (%R). Dynamic T2*-weighted perfusion MR images were collected from 263 acute ischemic stroke patients from four medical centers. An essential aspect of this study is to exploit a classifier-based framework to automatically identify predictive patterns in the overall intensity distribution of the permeability maps. The model is based on normalized intensity histograms that are used as input features to the predictive model. Linear and nonlinear predictive models are evaluated using a cross-validation to measure generalization power on new patients and a comparative analysis is provided for the different types of parameters. Results demonstrate that perfusion imaging in acute ischemic stroke can predict HT with an average accuracy of more than 85% using a predictive model based on a nonlinear regression model. Results also indicate that the permeability feature based on the percentage of recovery performs significantly better than the other features. This novel model may be used to refine treatment decisions in acute stroke.
authors.affiliation: Department of Neurology, University of California, LA, USA. fabien.scalzo@gmail.com
authors.forename: Fabien
authors.initials: F
authors.lastname: Scalzo
authors.forename: Jeffry R
authors.initials: JR
authors.lastname: Alger
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Saver
authors.forename: Krishna A
authors.initials: KA
authors.lastname: Dani
authors.forename: Keith W
authors.initials: KW
authors.lastname: Muir
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Demchuk
authors.forename: Shelagh B
authors.initials: SB
authors.lastname: Coutts
authors.forename: Marie
authors.initials: M
authors.lastname: Luby
authors.forename: Steven
authors.initials: S
authors.lastname: Warach
authors.forename: David S
authors.initials: DS
authors.lastname: Liebeskind
mesh_terms: D000368:Aged; D001812:Blood-Brain Barrier / Q000473:pathology*; D002545:Brain Ischemia / Q000453:epidemiology / Q000378:metabolism / Q000473:pathology*; D002199:Capillary Permeability; D015984:Causality; D002543:Cerebral Hemorrhage / Q000453:epidemiology / Q000378:metabolism / Q000473:pathology*; D015897:Comorbidity; D003287:Contrast Media / Q000493:pharmacokinetics; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D038622:Internationality; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D015995:Prevalence; D015203:Reproducibility of Results; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D020521:Stroke / Q000453:epidemiology* / Q000378:metabolism / Q000473:pathology*
chemical_list: D003287:Contrast Media