ORIGINAL RESEARCH article
Front. Neurol.
Sec. Stroke
Development and Validation of an Interpretable Machine Learning Model to Predict Malignant Cerebral Edema after Endovascular Treatment in Acute Anterior Circulation Large Vessel Occlusion Stroke
Provisionally accepted
Zhiwei Dong1
Zhengyu Huang1Hui Hu1
Yanan Wang2Chenxin Jiang1
Gang Li1
Feifeng Liu1
Tianrui Zhu1Hao Shen1
Chen Chen1
Yue Zhang1*- 1Tongji University Dongfang Hospital, Shanghai, China
- 2West China Hospital of Sichuan University, Chengdu, China
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Abstract Background: Malignant cerebral edema (MCE) is a life-threatening complication following endovascular treatment (EVT) in patients with acute anterior circulation large vessel occlusion (LVO) stroke. This study aimed to develop and validate a machine learning (ML)-based predictive model for early risk assessment of MCE in this population. Methods: We retrospectively collected data of 364 acute ischemic stroke patients with acute anterior circulation large vessel occlusion from a comprehensive stroke center in Shanghai, between August 2018 and December 2024. Eighty percent of patients were randomly assigned to the training set, and the remaining twenty percent to the internal validation set. Additional 162 patients from the One Pass Tirofiban In Management of Ischemic Stroke Thrombectomy In China (OPTIMISTIC) trial were included as an external validation set. Six machine learning models were developed, and the model with the highest area under the receiver operating characteristic curve (AUC) was selected as the optimal model. Its performance was evaluated in both internal and external validation sets. Decision curve analysis (DCA) and calibration curves were plotted to assess clinical utility. SHapley Additive exPlanations (SHAP) was employed to perform interpretative analysis of the model. Results: In this study, a total of 79 patients developed malignant cerebral edema (MCE), including 45 out of 291 (15.46%) patients in the training set, 13 out of 73 (17.81%) patients in the internal validation set, and 21 out of 162 (12.96%) patients in the external validation set. The random forest model performed best, achieving an AUC of 0.901 (95% CI: 0.858–0.943) in the training set, 0.849 (95% CI: 0.700–0.970) in the internal validation set, and 0.724 (95% CI: 0.606–0.841) in the external validation set. Conclusion: This study developed and externally validated an interpretable machine learning model to predict the risk of MCE in patients with acute anterior circulation LVO stroke following EVT.
Keywords: Acute ischemic stroke, machine learning, Malignant cerebral edema, predictive model, Shapley additive explanations
Received: 27 Aug 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Dong, Huang, Hu, Wang, Jiang, Li, Liu, Zhu, Shen, Chen and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yue Zhang
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