Interpretable machine learning for detecting symptomatic patients with carotid atherosclerosis on computed tomography angiography: a retrospective diagnostic study.

BACKGROUND: This study aimed to develop a machine learning (ML) model based on radiomics features of carotid plaques and perivascular adipose tissue (PVAT) on computed tomography angiography (CTA) to detect symptomatic carotid atherosclerosis. METHODS: This retrospective study included patients with extracranial carotid atherosclerotic plaques who underwent CTA between January 2022 and January 2024. Patients were divided into symptomatic and asymptomatic groups based on the occurrence of cerebrovascular events within two weeks prior to the CTA examination. Five ML models were constructed to identify symptomatic patients: clinical, PVAT radiomics, plaque radiomics, PVAT and plaque radiomics, and combined model. The most robust model was selected for Shapley Additive Explanations (SHAP) analysis to visualize the prediction process. RESULTS: The study cohort consisted of 229 patients (127 symptomatic; 102 asymptomatic). The Random Forest models demonstrated the best performance in detecting symptomatic patients. In the test cohort, the area under the curve (AUC) of the combined model (0.86; 95% confidence interval [CI]: 0.74–0.95) was significantly higher than that of the clinical model (AUC: 0.67, 95% CI: 0.50–0.81; p = 0.03), but similar to that of the PVAT and plaque radiomics model (AUC: 0.82, 95% CI: 0.70–0.93; p = 0.65). SHAP analysis of the combined model identified carotid plaque texture features and cholesterol levels as key factors in detecting symptomatic patients. CONCLUSIONS: Integrating radiomics of carotid plaques and PVAT with clinical data enhances the detection of symptomatic patients.