Predicting symptomatic carotid artery plaques with radiomics-based carotid perivascular adipose tissue characteristics: a multicenter, multiclassifier study.

objectiveThis study aims to differentiate between symptomatic and asymptomatic plaques using a computed tomography angiography (CTA)-based radiomics model of perivascular adipose tissue (PVAT).

methodsPatients were categorized into symptomatic and asymptomatic groups based on the presence or absence of acute ischemic stroke or transient ischemic attack in the anterior cerebral circulation within two weeks prior to the CTA examination. The clinical information of all patients was collected and analyzed, and the PVAT features of CTA images were further analyzed to clarify their correlation with plaque classification. K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), multinomial naive Bayes (MultinomialNB), and extreme gradient boosting (XGBoost) were trained and radiomics (Rad) score was calculated using the best classifier. A combined model was further developed based on the Rad-score and independent predictors, and the calibration, receiver operating characteristic curve, decision curve analysis, and clinical applicability were evaluated.

resultsThe white blood cell count and hyperlipidemia were clinically independent predictors, and ten PVAT radiomics features showed significant correlation. The XGBoost classifier showed the best performance among different classifiers, with an average AUC of 0.797 in the validation set. The combined model integrating Rad-score and clinically independent predictors was further obtained, with AUCs of 0.942, 0.797, and 0.836 in the training, external validation sets, respectively.

conclusionThe combined model performed excellently in predicting symptomatic carotid plaques. By early identification of high-risk patients and selecting appropriate clinical decisions, it holds significant clinical potential for improving stroke prevention.