Development of a Diagnostic Prediction Model for Post‐Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol

Development of a Diagnostic Prediction Model for Post‐Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol

ABSTRACT Objective: Stroke is a leading cause of morbidity and disability worldwide. Post‐stroke cognitive impairment (PSCI) significantly affects long‐term prognosis in acute anterior circulation large‐vessel occlusion stroke (LVO‐AIS). This study aims to develop a PSCI prediction model integrating...

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Main Authors: Junhao Li, Yuding Luo, Pingchuan Liu, Jiali Zhang, Chuanxi Duan, Hai Xiong, Maoxia Li, Binyang Zhang, Lu Li, Lulu Gong, Yupeng Niu, Bo Zheng, Jian Wang
Format: Article
Language:English
Published: Wiley 2025-06-01
Series:Brain and Behavior
Subjects:
post‐stroke cognitive impairment
magnetic resonance imaging
positron‐emission tomography
machine learning
endovascular treatment
Online Access:https://doi.org/10.1002/brb3.70613
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Summary:ABSTRACT Objective: Stroke is a leading cause of morbidity and disability worldwide. Post‐stroke cognitive impairment (PSCI) significantly affects long‐term prognosis in acute anterior circulation large‐vessel occlusion stroke (LVO‐AIS). This study aims to develop a PSCI prediction model integrating multimodal imaging, demographic, and clinical data collected during hospitalization. Methods and Design: This single‐center, prospective cohort study will enroll 379 anterior circulation LVO‐AIS patients undergoing emergency endovascular treatment (EVT) within 24 h of symptom onset. Participants will be categorized into PSCI and non‐PSCI groups and followed up at 90 and 180 days post‐procedure. Primary outcomes include Montreal Cognitive Assessment scores at 3 and 6 months, with the modified Rankin Scale as a secondary outcome. Baseline imaging data will be processed using 3D Slicer for MRI and PET/CT standardization, registration, and feature extraction. Machine learning models will be developed using these imaging features combined with demographic and clinical data and evaluated via metrics such as the area under the receiver operating characteristic curve, precision, and recall. Analyses will be conducted in a blinded manner. Conclusion: This study will develop a PSCI prediction model based on multimodal imaging and clinical data in EVT‐treated LVO‐AIS patients, providing a tool for early diagnosis and personalized interventions. While limited to a single‐center, future multicenter validation is necessary to establish its generalizability and clinical utility.
ISSN:2162-3279

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