Leveraging infarct topography for early warning: a robust model for predicting malignant cerebral edema after endovascular treatment in acute ischemic stroke.
Authors
Affiliations (5)
Affiliations (5)
- Department of Emergency, Xuzhou New Health Hospital, Xuzhou, 221007, China.
- Clinical Laboratory, Nanjing Tongren Hospital, School of Medicine, Southeast University, Nanjing, 210006, China.
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, China.
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, China. [email protected].
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, China. [email protected].
Abstract
The early prediction of malignant cerebral edema (MCE) following endovascular therapy for acute ischemic stroke is of paramount importance for facilitating timely interventions. The present study aimed to create a comprehensive map of lesion topography associated with MCE risk and to build a machine learning model based on these topography-informed radiomics to predict the MCE in stroke patients after endovascular therapy. Using voxel-based lesion analyses, we comprehensively quantified the spatial features of infarct location lesions. These topological features were integrated with radiomics to create a hybrid spatial radiomics model. Four machine learning algorithms bases on topography features, radiomics, and Topo-Rad features were developed to predict MCE in acute stroke patients, respectively. The performance of models was evaluated using the receiver operating characteristic curves, decision curve analysis and Net Reclassification Improvement. The SHapley Additive exPlanations (SHAP) method was employed to interpret and visualize the output of the optimal model. The topography maps for acute stroke patients showed the right temporal lobe and right caudate nucleus were significantly associated with MCE (Pā<ā0.05). For four ML algorithms, the SVM model based on topo-Rad achieved the highest predictive performance (AUC in training/validation set: 0.872/0.842), while no statistically significant difference was observed compared to the model based on topography (0.857/0.812). The SHAP plots demonstrated that the most significant contributors to model performance were related to temporal_pars_of_MCA_R, occipital_pars_of_PCA_R, parietal_pars_of_MCA_R, temporal_pars_of_MCA_L, and parietal_pars_of_MCA_L. The infarct topography plays a dominant role in predicting MCE following endovascular therapy, with radiomic features providing limited additional predictive value.