Non-enhanced CT-based radiomics signature of epicardial adipose tissue for screening coronary heart disease

Abstract

Objective: Our study aimed to establish a predictive model based on non-enhanced CT imaging features of epicardial adipose tissue (EAT) to differentiate patients with coronary heart disease (CHD) from those without. Methods: In this radiomics study, we collected clinical and radiomic data from a total of 281 patients diagnosed with CHD at the China-Japan Friendship Hospital, along with 188 healthy individuals who underwent physical examinations at our hospital. The participants were allocated to either a training or validation group at random, following a 7:3 ratio. We performed multivariate logistic regression analysis to create a clinical model, using a significance threshold of p<0.05. Additionally, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to highlight important radiomic features for constructing a radiomics model. Lastly, we integrated the clinical and radiomics models to establish a combined model. To assess the model's effectiveness, we used the area under the curve (AUC) , DeLong's test, and decision curve analysis (DCA). Results: In this radiomics study, the AUC of the clinical model were 0.883 (95% CI: 0.848-0.918) for the training cohort and 0.872 (95% CI: 0.812-0.932) for the validation cohort. In the radiomics model, the AUC for the training cohort was 0.853 (95% CI: 0.814-0.892) and for the validation cohort, it was 0.822 (95% CI: 0.751-0.893). DeLong's test revealed no significant difference in AUC between the clinical and radiomics models in both the training cohort (p=0.218) and the validation cohort (p=0.24). The combined model exhibited good discriminative ability, and the AUC were 0.930 (95% CI: 0.905-0.956) for the training cohort and 0.914 (95% CI: 0.863 -0.965) for the validation cohort. In the DeLong's test, we found that the AUC of the combined model was significantly higher in both cohorts compared to the other models (p < 0.05). Furthermore, the DCA curve revealed that using the combined model to identify patients with CHD provided greater advantages compared to using the two separate models. Conclusions: Our findings indicate that the combined model, which incorporated clinical features and the radiomics signature of EAT, can serve as a valuable tool for distinguishing patients with and without CHD.