ObjectiveTo study the characteristics of carotid atherosclerotic plaque and investigate the relationship between visfatin and plaque stabilization. Methodsfifty-six patients with carotid stenosis were divided into symptomatic group (n=31) and asymptomatic group (n=25) based on the clinical manifestation and onset time.All plaque specimens were stained with HE and Masson trichrome staining and studied pathologically.The plaques were grouped into stable and unstable plaques based on thickness of the fibrous cap and the area of lipid-rich core in the plaques.The expression of visfatin was detected by immunohistochemistry staining. Results①The proportion of unstable plaques were significantly higher in symptomatic group than in asymptomatic group (67.74% vs 36.00%, P < 0.05).②Compared with stable plaques, unstable plaques had thinner fibrous cap, larger lipid necrotic core, and higher proportion of hemorrhage: (49.87±8.75)μm vs (74.54±6.80)μm (P < 0.001), (65.63±12.97)% vs (31.81±5.13)% (P < 0.001), and 63.33% vs 30.77% (P < 0.05).③The integral optical density value of expressed visfatin in unstable plaques was significantly more than in stable plaques (84 165.47±9 183.12 vs 55 694.08±4 818.57, P < 0.001). ConclusionsThe plaque destabilization is closely related to the clinical symptoms of atherosclerosis.The thickness of fibrous cap, area of lipid-rich core, and hemorrhage play an important role in the plaque stabilization.The visfatin is related to atherogenesis and plaque destabilization.
Objective To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. ResultsFinally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.