Objective This study aimed to explore a prediction model for the risk of recurrent AIS within 2 years based on MRI radiomics.Methods We retrospectively collected MRI and clinical data from 148 patients diagnosed with AIS at the affiliated hospital of Xuzhou Medical University between August 2018 and July 2019.The patients were divided into a recurrent group and a non-recurrent group based on recurrence within 2 years after discharge.To validate the model per-formance,the samples were randomly divided into training and testing sets with a 7∶3 ratio.Two radiologists marked the le-sions on diffusion-weighted imaging(DWI)MRI sequences using MaZda software,extracted radiomic features from the ima-ges,and reduced the dimensionality of the radiomic features using LASSO-Logistic regression.Twenty common machine learning algorithms were used to construct prediction models combining MRI radiomic features with clinical features.The individual MRI radiomic feature model and clinical feature model were compared based on the optimal model parameters.Model performance was evaluated using metrics such as sensitivity,specificity,and area under the receiver operating charac-teristic curve(AUC).Results Fourteen features were finally extracted from the 300 radiomic features using LASSO-Lo-gistic from DWI images.The distribution of radiomic signature values differed significantly between non-recurrent and recur-rent patients(P＜0.05).The XGBoost model in the combined MRI and clinical model showed good predictive accuracy in both the testing and training sets,outperforming the single-feature models.SHAP analysis revealed that Radscore had the greatest impact on the model and made positive contributions to the AIS recurrence probability,followed by HDL-C with negative contributions.TG and LDL-C made positive contributions,while age and LP(a)made positive contributions.Addi-tionally,TC and gender showed minimal impact on the model.Conclusion The XGBoost model based on combined MRI radiomic features and clinical indicators performed best in predicting recurrent AIS within 2 years.Combining MRI radiomic features with clinical data improved the predictive performance of the model.
Magnetic resonance imagingRadiomicsAcute ischemic strokeRecurrenceMachine LearningPre-dictive model
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