Malignant cerebral edema after endovascular thrombectomy: a multimodal prediction model based on post-thrombectomy cerebral hyperdensity and natural language processing.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
- Department of Neurology, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
- Department of Neurointervention, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
- Department of Radiology, The First Hospital of Jiaxing, The Affliated Hospital of Jiaxing University, Jiaxing, Zhejiang, China.
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China.
Abstract
Early prediction of malignant cerebral edema (MCE) following endovascular thrombectomy (EVT) is critical for guiding timely interventions. This study aimed to develop and validate a multimodal prediction, integrating non-contrast CT (NCCT) features and natural language processing (NLP)-encoded clinical data to predict MCE after EVT. In this multi-center retrospective study, 373 patients treated with EVT were included, comprising internal (<i>n</i> = 287) and external (<i>n</i> = 86) cohorts. MCE was defined as a midline shift of ≥5 mm. Deep imaging features were extracted using a ResNet-101 model, the NCCT slice demonstrating the maximal extent of post-thrombectomy cerebral hyperdensity (PCHD). Concurrently, a pre-trained NLP model, BioClinicalBERT, was utilized to generate semantic embeddings from synthesized clinical narratives derived from standard admission variables. A multimodal fusion model integrating these features was subsequently evaluated against single-modality models and the diagnostic performance of human experts. In the independent external cohort, the multimodal fusion model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 [95% confidence interval (CI): 0.700-0.901] and an accuracy of 80.2%, demonstrating superior performance compared to clinical-only (AUC = 0.654), ResNet-only (AUC = 0.707), and BERT-only (AUC = 0.560) models. SHapley Additive exPlanations (SHAP) analysis revealed NLP-derived semantic features as the principal predictors. Furthermore, AI assistance improved the diagnostic performance of senior neuroradiologists (AUC: 0.709-0.763; <i>p</i> < 0.05) and increased their specificity increased from (78.1% to 84.4%). A multimodal framework integrating targeted NCCT imaging features with NLP-encoded clinical data yields an accurate multimodal tool for early MCE prediction. This multimodal approach enhances human decision-making in emergency workflows.