Abstract DP363: Bayesian Derived Multiparametric MRI Model Predicts Development of Parenchymal Hematoma in Acute Stroke Following Reperfusion

Introduction: Parenchymal hemorrhage (PH) is a feared complication of reperfusion therapies in patients with acute ischemic stroke (AIS), which can significantly worsen the prognosis. This study aimed to evaluate whether the MRI perfusion biomarkers could predict PH development in AIS patients undergoing reperfusion therapy. Methods: In this retrospective cohort study, AIS patients with anterior circulation large vessel occlusions included if the patients underwent pre-treatment diffusion-weighted imaging (DWI) and dynamic susceptibility contrast (DSC) perfusion MRI as well as follow up imaging to assess PH. Using a novel vascular model based on Bayesian framework, parametric maps including cerebral blood flow (CBF) and blood-brain barrier leakage were obtained. Apparent diffusion coefficient (ADC) from diffusion MRI was also included. Mask of baseline infarct (ADC < 620 × 10 -6 ) generated and registered to perfusion maps to extracted voxel values. The associations between PH and clinical and imaging parameters were assessed in univariate and multivariate analyses. A predictive model was developed using logistic regression analysis and a decision tree classifier following 5-fold stratified cross-validation. The performance of this model was tested in a separate external cohort of patients. Results: Among 124 patients included, 35 (28%) developed PH. Multivariate logistic regression analysis showed extreme values of perfusion parameters including 95 th% -leakage (OR=1.31, 95% CI: 1.1-1.63, p=0.006), 95 th% -CBF (OR=0.37, 95% CI: 0.13-0.89, p=0.002), and 5 th% -ADC (OR=0.98, 95% CI: 0.96-0.99, p