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Ischemic stroke (IS) exhibits a high disability rate, mortality, and recurrence rate, imposing a serious threat to human survival and health. Its occurrence is affected by various factors. Although the previous research has demonstrated that the occurrence of IS is mainly associated with lumen stenosis caused by carotid atherosclerotic plaque (AP), recent studies have revealed that many patients will still suffer from IS even with mild carotid artery lumen stenosis. Blood supply disturbance causes 10% of IS to the corresponding cerebral blood supply area caused by carotid vulnerable plaque. Thrombus blockage of distal branch vessels caused by rupture of vulnerable carotid plaque is the main cause of ischemic stroke. Therefore, how to accurately evaluate vulnerable plaque and intervene as soon as possible is a problem that needs to be solved in clinic. The vulnerability of plaque is determined by its internal components, including thin and incomplete fibrous cap, necrotic lipid core, intra‐plaque hemorrhage, intra‐plaque neovascularization, and ulcerative plaque formation. The development of imaging technology enables the routine detection of AP vulnerability. By analyzing the pathological changes, characteristics, and formation mechanism of carotid plaque vulnerability, this article aims to explore the modern imaging methods which can be used to identify plaque composition and plaque vulnerability to provide a reference basis for disease diagnosis and differential diagnosis.

Vulnerable carotid plaque is closely related to the occurrence of Ischemic stroke. Therefore, accurate and rapid identification of the nature of carotid plaques is essential. AS is a chronic immune inflammatory process. Systemic immune-inflammation index (SII) is a novel index of immune inflammation obtained from routine whole blood cell count analysis, which comprehensively reflects the state of inflammation and immune balance in the body. This study sought to explore the relationship between SII level and carotid plaque vulnerability, plaque composition characteristics, and acute ischemic stroke (AIS) severity. A total of 131 patients diagnosed with AIS presenting with a carotid atherosclerotic plaque were enrolled in this study. Using carotid ultrasound (CDU) to assess the carotid-responsible plaque properties, we divided the patients into stable plaques group and vulnerable plaques group, and analyzed the correlation between SII levels and plaque vulnerability. And we further analyzed to evaluate the correlation between high SII levels and plaque characteristics and AIS severity. In addition, Cohen's Kappa statistics was used to detect the consistency of Carotid ultrasound (US) and cervical High-resolution magnetic resonance imaging (HRMRI) in evaluating plaque vulnerability. The findings showed that the vulnerable group had higher levels of SII compared with the stable group. The high SII group had more vulnerable plaques and a high frequency of plaque fibrous cap rupture compared with the low SII group. Logistic analysis showed that a high SII level was an independent risk factor for vulnerable plaques (odds ratio [OR] = 2.242) and plaque fibrous cap rupture (OR=3.462). The results also showed a high consistency between Carotid US and HRMRI methods in the assessment of plaque vulnerability [Cohen's kappa value was 0.89 (95% CI = 0.78–0.97)] and the level of SII was positively associated with NIHSS score (r = 0.473, P < 0.001). Our study suggests that elevated levels of SII may have adverse effects on the vulnerability of carotid plaques, especially in stroke patients with vulnerable plaques with ruptured fibrous caps, which may aggravate the severity of AIS.

Vulnerable carotid plaques are closely related to the occurrence of ischemic stroke. Therefore, accurate and rapid identification of the nature of carotid plaques is essential. This study aimed to determine whether texture analysis based on a vascular ultrasound can be applied to identify vulnerable plaques. Data from a total of 150 patients diagnosed with atherosclerotic plaque (AP) by carotid ultrasound (CDU) and high-resolution magnetic resonance imaging (HRMRI) were collected. HRMRI is the in vivo reference to assess the nature of AP. MaZda software was used to delineate the region of interest and extract 303 texture features from ultrasonic images of plaques. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the overall cohort was randomized 7:3 into the training (n = 105) and testing (n = 45) sets. In the training set, the conventional ultrasound model, the texture feature model, and the conventional ultrasound-texture feature combined model were constructed. The testing set was used to validate the model’s effectiveness by calculating the area under the curve (AUC), accuracy, sensitivity, and specificity. Based on the combined model, a nomogram risk prediction model was established, and the consistency index (C-index) and the calibration curve were obtained. In the training and testing sets, the AUC of the prediction performance of the conventional ultrasonic-texture feature combined model was higher than that of the conventional ultrasonic model and the texture feature model. In the training set, the AUC of the combined model was 0.88, while in the testing set, AUC was 0.87. In addition, the C-index results were also favorable (0.89 in the training set and 0.84 in the testing set). Furthermore, the calibration curve was close to the ideal curve, indicating the accuracy of the nomogram. This study proves the performance of vascular ultrasound-based texture analysis in identifying the vulnerable carotid plaques. Texture feature extraction combined with CDU sonogram features can accurately predict the vulnerability of AP.

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