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

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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<0.001) as independent biomarkers predictive of PH. The 5-fold cross validated combined model of these 3 variables (95 th% -leakage≥7.9, 95 th% -CBF≤0.7, and ADC≤360) resulted in an average AUC of 0.84 ± 0.05, 91% sensitivity and 78% specificity. In external validation cohort (n=20), the model correctly identified 80% of PHs with 90% specificity. Conclusions: We developed a multiparametric model by integrating baseline MRI biomarkers including ADC, CBF, and leakage to identify AIS patients at elevated risk of parenchymal hematoma after reperfusion. Extreme values of these markers were independently associated with PH and provided complementary information beyond single-parameter models. Incorporating multiparametric MRI into periprocedural workflows may enable more precise risk stratification and guide individualized treatment decisions.

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