Identification of symptomatic carotid plaque by CTA-based radiomics: a multicenter study

To develop and validate a combined model integrating traditional clinical characteristics, imaging features and radiomic features based on head and neck computed tomography angiography (CTA) to predict ischemic events in ipsilateral cerebral vessels.

In this multicenter retrospective study, 223 patients from 3 independent centers were divided into training set (n = 134), internal test set (n = 34) and external validation set (n = 55). Based on recent symptoms (presence or absence of ipsilateral cerebral ischemia), patients were categorized into symptomatic group (n = 110) and asymptomatic group (n = 113). The traditional clinical characteristics, imaging features and radiomic features of all patients were collected. The traditional quantitative variables independently related to symptomatic carotid plaque were identified using univariate analysis and multivariate logistic regression analysis, and the intraclass correlation coefficient (ICC) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were applied to select robust radiomic features. Subsequently, three predictive models - the traditional model, radiomic model, and combined model integrating clinical, imaging and radiomic features - were constructed. Model performance was evaluated using receiver operating characteristic curves (ROCs) analysis, area under the curves (AUCs), calibration curves and decision curves analysis, and the accuracies of the models were verified in internal test set and external validation set.

Univariate analysis and multivariate logistic regression analysis showed that platelet distribution width (PDW) (odds ratio [OR] = 0.88; 95% confidence interval [CI], 0.80-0.97) and plaque ulceration (OR = 5.67; 95% CI, 2.86-11.23) were independently related to symptomatic plaque. Twelve radiomic features significantly related to symptomatic plaque were selected. The combined model demonstrated superior performance compared with both the radiomic model and the traditional model, the AUCs of the training set and internal test set were 0.819(95% CI: 0.749-0.888) and 0.785(95% CI: 0.620-0.950), and also demonstrated robust performance in external validation set (AUC: 0.868; 95% CI: 0.765-0.970).

The Combined model demonstrated the highest diagnostic performance in identifying symptomatic plaque, which helps clinicians to analyze patients' condition more comprehensively and provides additional value for identifying high-risk individuals and improving prognosis.