CTA-derived radiomics features associated with prediction of ipsilateral stroke recurrence in retrospective carotid atherosclerosis cohortNew scan analysis might predict stroke risk better than usual checks

ObjectiveTo develop and validate an interpretable machine learning (ML) model integrating computed tomography angiography (CTA)-derived radiomics features of carotid plaque and perivascular adipose tissue (PVAT) for predicting ipsilateral stroke recurrence in patients with carotid atherosclerosis.MethodsIn this retrospective study, patients with unilateral carotid atherosclerosis who underwent head and neck CTA between May 2016 and March 2024 were included and followed for recurrent ischemic stroke detected by follow-up MRI. Radiomics features of carotid plaque and PVAT were automatically extracted using a deep learning-based segmentation model and then gathered to constructed a ML model to predict stroke risk. A conventional clinical model based on carotid stenosis degree and clinical factors was also developed. Additionally, a combined model incorporating both radiomics and clinical factors was constructed. The optimal predictive model was chosen among five ML algorithms based on the area under receiver operating characteristics curve (AUC). Model performance was validated through repeated 10-fold cross-validation and tested in an independent testing cohort. Model interpretability was examined using Shapley Additive Explanations (SHAP).ResultsOf 162 patients (mean age, 69.28 years ± 8.30 [SD]; 136 [83.95%] male) were included, of whom 63 (38.9%) experienced ipsilateral stroke recurrence during follow-up (median, 1 years). The combined model using support vector machines achieved the highest AUC of 0.87(95% CI: 0.74–0.97) in the testing set, higher than the radiomics-only model (AUC, 0.80; 0.63–0.94) and the clinical model (AUC, 0.77; 0.58–0.91; all p