Abstract
This study aims to create a radiomics nomogram using dual-energy computed tomography (DECT) virtual monoenergetic images (VMI) to accurately identify symptomatic carotid plaques. Between January 2018 and May 2023, data from 416 patients were collected from two centers for retrospective analysis. Center 1 provided data for the training (n = 213) and internal validation (n = 93) sets, and center 2 supplied the external validation set (n = 110). Plaques imaged at 40 keV, 70 keV, and 100 keV were outlined, and the selected radiomics features were used to establish the radiomics model. The classifier with the highest area under the curve (AUC) in the training set generated the radiomics score (Rad-Score). Logistic regression was used to identify risk factors and establish a clinical model. A radiomics nomogram integrating the Rad-score and clinical risk factors was constructed. The predictive performance was evaluated using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Plaque ulceration and plaque burden are independent risk factors for symptomatic carotid plaques. The 40 + 70 keV radiomics model achieved excellent diagnostic performance, with an average AUC of 0.805 across all validation sets. Furthermore, the radiomics nomogram, integrating the Rad-score with clinical predictors, demonstrated robust diagnostic accuracy, with AUCs of 0.909, 0.850, and 0.804 in the training, internal validation, and external validation sets, respectively. DCA results suggested that the nomogram was clinically valuable. Our study developed and validated a DECT VMI-based radiomics nomogram for early identification of symptomatic carotid plaques, which can be used to assist clinical diagnosis and treatment decisions. The study introduces an innovative radiomics nomogram utilizing DECT VMI to discern symptomatic carotid plaques with high precision.