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

## TL;DR

The study developed a model using CT scans of heart fat to help identify people with coronary heart disease.

## Contribution

A novel radiomics model combining clinical data and non-enhanced CT features of epicardial adipose tissue for CHD screening.

## Key findings

- The combined model achieved an AUC of 0.930 in training and 0.914 in validation for CHD detection.
- The radiomics model performed comparably to the clinical model in both training and validation cohorts.
- The combined model showed significantly better performance than individual models according to DeLong's test.

## Abstract

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.

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).

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.

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.