| Sumario: | Objective To investigate the predictive value of quantitative parameters from high-resolution magnetic resonance vessel wall imaging (HR - VWI) for acute ischemic stroke (AIS). Methods The 120 patients with intracranial atherosclerosis who underwent both cranial CT and HR - VWI examinations at Second Affiliated Hospital of Nanjing Medical University from June 2021 to October 2024 and completed at least 6 months follow-up. These patients were divided into the training set (n = 84) and the testing set (n = 36) at a ratio of 7:3. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO), and the final predictive model was constructed with the XGBoost model. Model predictive performance was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC), and generalization ability was further evaluated through learning curves. Shapley additive explanation (SHAP) was used to calculate the global contribution of each feature variable to the model's prediction. Results Among the 120 patients, 27 cases (22.50%) experienced AIS within the 6-month follow-up period. Specifically, there were 19 cases (22.62%) in the training set and 8 cases (22.22%) in the testing set. Multivariate Logistic regression identified calcified plaque volume ratio (OR = 0.123, 95%CI: 0.039-0.393; P = 0.000), plaque enhancement ratio (OR = 1.130, 95%CI: 1.046-1.221; P = 0.002), plaque hemorrhage (OR = 9.519, 95%CI: 2.453-36.968; P = 0.008), and luminal stenosis ratio (OR = 1.106, 95%CI: 1.032-1.185; P = 0.004) as predictors of AIS. ROC curves showed the AUC of Logistic regression model was 0.950 and 0.812 in the training set and the testing set, with sensitivity of 0.947 and 0.750 and specificity of 0.800 and 0.857, respectively. Similarly, the XGBoost model achieved an AUC of 0.986 in the training set and 0.844 in the testing set, with a sensitivity of 0.947 and 0.875, and a specificity of 0.923 and 0.857, respectively. Learning curves analysis further confirmed that both models exhibited good stability and generalization ability as the training sample size increased, with XGBoost showing slightly better overall predictive performance. SHAP analysis indicated that the calcified plaque volume ratio contributed the most to model prediction. Conclusions Machine learning models that integrate calcified plaque volume ratio, plaque enhancement ratio, plaque hemorrhage and lumen stenosis ratio can significantly improve the predictive performance for AIS, exhibit good generalization ability, and provide a potential reference tool for clinical risk assessment.
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