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Updated: Jul 9, 2026
Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging (MRI)
Published on: December 16, 2021
Multiparametric MRI Model Predicts Parenchymal Hematoma in Acute Ischemic Stroke After Reperfusion
Mona Asghariahmadabad1, Lucia Zima1, Ameera Ismail1
1From the Department of Radiology and Biomedical Imaging (M.A., K.N., S.W.H., C.P.H., K.N.), Neurology (N.K., S.A.J.), Neurological Surgery (L.E.S., E.W.), University of California, San Francisco, San Francisco, CA, USA; Department of Radiological Sciences (A.I., K.N.), David Geffen School of Medicine at UCLA, Neurology (M.B.-H., J.L.S., D.S.L.), David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA; Department of Mechanical Engineering (M.K.), University of Washington, Seattle,WA; The Russell H. Morgan Department of Radiology and Radiological Science (V.Y.), Johns Hopkins University School of Medicine, Baltimore, MD, USA and Department of Radiology - CDI (Centre de Diagnòstic per Imatge) (J.P.), Universitat de Barcelona, Barcelona, Spain.
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.
