Multiparametric MRI Model Predicts Parenchymal Hematoma in Acute Ischemic Stroke After Reperfusion

BACKGROUND AND PURPOSE: Parenchymal hematoma (PH) is a severe complication of reperfusion therapy in acute ischemic stroke and is associated with poor functional outcome. We evaluated whether baseline quantitative imaging biomarkers derived from MR diffusion and perfusion could predict PH after reperfusion therapy. MATERIALS AND METHODS: In this retrospective study, consecutive adults with acute ischemic stroke due to large-vessel occlusion who underwent endovascular thrombectomy between 2015 and 2020 and had baseline MRI and follow-up imaging were included. Quantitative imaging biomarkers were extracted from the baseline ischemic core, including apparent diffusion coefficient (ADC), relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), and relative K2 (rK2) derived from Bayesian-based DSC perfusion processing. The primary outcome was PH on follow-up imaging. Baseline imaging and clinical variables were compared between patients with and without PH. An elastic-net logistic regression model was developed using nested stratified group 5-fold cross-validation, and model performance was summarized by receiver operating characteristic analysis and SHAP-based feature importance. RESULTS: The final cohort included 100 patients, of whom 32 developed PH. Baseline demographic, clinical, and conventional imaging characteristics did not differ significantly between groups. Patients with PH had significantly lower values of rCBF, rCBV, and ADC and higher values of rK₂ in comparison to those without PH. The final model incorporated 5th% ADC, rCBF, rCBV, and 95th% rK₂ showed discrimination with an AUC of 0.88 ± 0.07 (mean ± SD) and overall accuracy of 77%. CONCLUSIONS: We developed a quantitative model using ADC, rCBV, rCBF, and rK₂ from bassline MRI that can identify stroke patients with an increased risk of PH with approximately 77% accuracy.