Radiomics-Based Classification of Pathological Patterns in Common Carotid Artery Wall

Abnormal echogenic patterns such as the triple signal pattern can be identified in the common carotid artery (CCA) using ultrasound. These patterns have been associated not only with fibromuscular dysplasia but also with primary hypertension and cardiovascular risk factors, suggesting their potential as markers of vascular aging. Typically identified through visual inspection, their detection is time-consuming and operator-dependent. This study aimed to develop a machine learning approach to identify carotid wall patterns based on carotid-ultrasound image-derived features from a general population cohort.

Ultrasound data from 784 participants were analyzed, and 178 radiomic features were extracted from a standardized region of interest on the far wall of the CCA. Vascular wall patterns were visually classified by the physician as healthy or abnormal. Features were filtered based on inter-operator reproducibility, Pearson correlation and feature-relevance. Logistic regression (LR) and support vector machine models were trained using an 80/20 train-test split.

Healthy and abnormal class represented 56% and 44% of the data, respectively. The inter-operator reproducibility increased with greater overlap in ROI placement. Logistic regression achieved accuracy: 0.70, sensitivity: 0.62, specificity: 0.76, and AUC: 0.78 on the training set, and maintained strong performance on the test set (AUC: 0.72, sensitivity: 0.71, specificity 0.59, accuracy: 0.64), based on 40 selected features.

These results demonstrate that machine learning classifier can discriminate between healthy and abnormal vascular wall patterns and in the future, this tool may be used to investigate clinical relevance of carotid wall patterns in larger cohorts.