Classification of carotid artery plaques: promising alternative methods to computed tomography angiography through...

Carotid artery plaques (CAPs) significantly contribute to stroke. Accurate plaque characterization is crucial for predicting stroke risk. This study explored the effectiveness of a non-contrast computed tomography (NCCT)-based radiomics model in identifying and classifying CAPs.

The dataset included 600 patients with CAPs from two centers, who were divided into training (n=400), internal test (n=100), and external test sets (n=100). Radiomics features were extracted from NCCT images. Five algorithms-Gaussian processes (GP), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF)-were employed to develop a two-level binary classification model (TBCM) and four-class classification model (FCM) for predicting the four CAP subtypes. TBCM comprised three binary classifiers. Receiver operating characteristic (ROC) curve analysis was used to evaluate model performance.

In FCM, 38 optimal features were selected. For TBCM, 14, 13, and 22 optimal features were selected from classifiers 1-3, respectively. The GP [with areas under the ROC curves (AUCs) of 0.892-1 for three classifiers] and RF models (with AUCs of 0.883-1 for three classifiers) exhibited superior performance in the internal test set. The model combining GP and RF yielded AUCs of 0.893-1. In the external test set, the GP model achieved AUCs of 0.902-1 for three classifiers, compared with 0.939-1 for the RF model. The combined model achieved AUCs of 0.939-1 for three classifiers.

This study highlights the efficacy of the NCCT-based radiomics model in discerning the composition of CAPs.