Development of a nomogram model using the dual-energy computed tomography angiography parameters of carotid plaque, the vascular lumen, and perivascular fat to predict acute stroke events.

Background: Stroke is the second leading cause of death worldwide. Carotid plaque is a major risk factor for acute cerebrovascular events. Currently, comprehensive quantitative analyses of the dual-energy computed tomography angiography (DECTA) parameters of plaque, the vascular lumen, and perivascular adipose tissue (PVAT) remain limited. This study aimed to explore the association between these multidimensional parameters and stroke, and to develop a risk prediction model. Methods: A retrospective analysis was performed of data from patients who underwent DECTA and cranial magnetic resonance imaging (MRI) between January 2023 and September 2024. Regions of interest (ROIs) were defined on the most prominent axial slice of carotid plaque PVAT. Patients with acute cerebral infarction were categorized as the symptomatic (STA) group, while those without were classified as the asymptomatic (ATA) group. The data analysis was conducted using SPSS and R. Univariate variables with a P value <0.05 were included in the multivariate logistic regression analysis, and a nomogram was then constructed. A receiver operating characteristic (ROC) curve analysis was used to evaluate predictive performance. Results: A total of 69 patients were included in the study, with 20 in the STA group (29.0%) and 49 in the ATA group (71.0%). The STA group had significantly lower fat fraction (FF) values and higher virtual non-contrast (VNC), electron density (Rho), and CT values corresponding to 40 keV on the energy spectrum curve (40KH) (all P values ≤0.001) than the ATA group. Additionally, the slope of the energy spectrum curve (K) value was lower (P<0.001) and the lipid-rich volume to non-calcified plaque volume (LRV/NCV) ratio was higher in the STA group than the ATA group (P=0.045). These significant variables were subsequently included in the logistic regression analysis, and a dynamic nomogram for predicting STA was then constructed. The combined variable model had an area under the curve (AUC) of 0.934, a sensitivity of 95.0%, and a specificity of 77.6%, demonstrating superior predictive performance compared with individual variables. Conclusions: The quantitative assessment of PVAT, plaque, and the vascular lumen using carotid DECTA significantly improves the ability of models to predict acute stroke events.