Classification of carotid artery plaques: promising alternative methods to computed tomography angiography through radiomics approach using neck non-contrast computed tomography

Abstract

Background: 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.

Methods: 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.

Results: 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.

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

Keywords: Carotid artery plaques (CAPs); model construction; non-contrast computed tomography scan (NCCT scan); radiomics.

Figure 1

Non-contrast CT, contrast-enhanced CT, and manual annotation by radiologists are provided for four plaque subtypes: non-calcified plaque (A), calcified plaque (B), mixed plaque (C), and no plaque (D). The green arrow indicates the carotid artery containing the plaque. The colored region represents the delineated ROI. CT, computed tomography; ROI, region of interest.

Figure 2

Model development process. The green arrow indicates the carotid artery containing the plaque. The colored region represents the delineated ROI. FCM, four-class classification model; GLCM, gray level co-occurrence matrix; GLDM, gray level dependence matrix; GLRLM, gray level run length matrix; GLSZM, gray level size zone matrix; NGTDM, neighboring gray tone difference matrix; ROI, region of interest; TBCM, two-level binary classification model.

Figure 3

Confusion matrix plots for FCM and TBCM across five machine learning algorithms, including DT (A), GP (B), LR (C), RF (D), and SVM (E), within the internal test set. DT, decision tree; FCM, four-class classification model; GP, Gaussian processes; LR, logistic regression; RF, random forest; SVM, support vector machine; TBCM, two-level binary classification model.

Figure 4

The ROC curves for the GP, RF, and combined models of TBCM within the internal test set. (A) Calcified vs. non-calcified components. (B) Calcified plaque vs. mixed plaque. (C) Non-calcified plaque vs. no plaque. AUC, area under the receiver operating characteristic curve; GP, Gaussian processes; RF, random forest; ROC, receiver operating characteristic; TBCM, two-level binary classification model.

Figure 5

The ROC curves for the GP, RF, and combined models of TBCM within the external test set. (A) Calcified vs. non-calcified components. (B) Calcified plaque vs. mixed plaque. (C) Non-calcified plaque vs. no plaque. AUC, area under the receiver operating characteristic curve; GP, Gaussian processes; RF, random forest; ROC, receiver operating characteristic; TBCM, two-level binary classification model.

Figure 6

Confusion matrix plots for the GP (A), RF (B), and combined models (C) of TBCM within the external test set. GP, Gaussian processes; RF, random forest; TBCM, two-level binary classification model.