Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability - Shan, Dezhi; Wang, Siyu; Wang, Junjie; Lu, Jun; Ren, Junhong; Chen, Juan; Wang, Daming; Qi, Peng (Frontiers in Neurology, 2023)
Vulnerable carotid atherosclerotic plaque (CAP) is a significant contributor leading to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using Contrast-enhanced Ultrasound (CEUS). Computed Tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomics features associated with neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomics features. CTA and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. Data were split into training cohort and testing cohort by 7:3. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images and the Pyradiomics package was used to extract radiomics features in Python. Machine learning algorithms, containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP), were used to construct the models. The confusion matrix, receiver operating characteristic curve (ROC), accuracy, precision, recall and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. 1316 radiomics features were extracted, and 10 radiomics features were selected for machine learning model construction. After evaluating several models on the testing cohorts, it was discovered that model_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomics features associated with neovascularization of CAP were obtained. The model_RF, utilizing radiomics features extracted from CTA, provides a non-invasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential in offering clinical guidance for early detection and improving patient outcomes.