# Radiomics analysis of pericoronary adipose tissue for detecting ischaemia with non-obstructive coronary arteries in NAFLD patients

## TL;DR

This study shows that radiomics analysis of pericoronary fat improves detection of heart issues in NAFLD patients with non-blocked arteries.

## Contribution

A novel radiomics model combining imaging and clinical data improves INOCA detection in NAFLD patients.

## Key findings

- The radiomics model outperformed PCATa in identifying INOCA in both training and validation cohorts.
- The combined imaging-clinical model achieved the highest diagnostic accuracy with AUCs of 0.873 and 0.824.
- The model showed improved reclassification and decision-making metrics over clinical-only models.

## Abstract

Chronic low-grade inflammation in nonalcoholic fatty liver disease (NAFLD) plays a critical role in the development of cardiovascular complications, particularly ischaemia with non-obstructive coronary arteries (INOCA). This study aimed to develop and evaluate models combining pericoronary adipose tissue (PCAT) radiomics, PCAT attenuation (PCATa), CCTA plaque parameters, and clinical risk factors to identify INOCA in NAFLD patients.

This retrospective study included 159 patients with NAFLD who underwent CCTA. The patients were randomly divided into the training (70%) and validation (30%) cohorts. Clinical features, CCTA imaging indicators, and right coronary artery PCAT radiomic features were analyzed. Five models were constructed using logistic regression: Model 1 (PCATa model), Model 2 (radiomics model), Model 3 (clinical factors model), Model 4 (combined imaging model), and Model 5 (combined imaging-clinical model). The models’ diagnostic performance was assessed using the area under the curve, reclassification metrics, and decision curve analysis (DCA).

The PCAT radiomics model exhibited higher diagnostic efficacy than the PCATa model in identifying INOCA (training cohort: AUC 0.734 vs. 0.674; validation cohort: AUC 0.706 vs. 0.637). The combined imaging model showed improved performance over the clinical factors model (training AUC 0.830, validation AUC 0.813). The model integrating imaging and clinical factors achieved the highest diagnostic accuracy (AUCs of 0.873 and 0.824 in the training and validation cohorts, respectively), demonstrating incremental value based on improved NRI, IDI, and DCA metrics. Calibration analysis indicated good agreement between predicted and observed outcomes.

The radiomics model provided better discrimination than the PCATa model for identifying INOCA among patients with NAFLD. Models incorporating radiomics and CCTA imaging parameters outperformed those based solely on clinical factors. The comprehensive imaging-clinical model achieved the best overall performance and may serve as a promising non-invasive approach for INOCA risk stratification in NAFLD, although external validation is still required.

The online version contains supplementary material available at 10.1186/s12872-025-05292-5.