# Coronary CTA-based radiomic signature of pericoronary adipose tissue predict rapid plaque progression

## TL;DR

This study shows that analyzing pericoronary adipose tissue using radiomic features from CT scans can better predict rapid coronary plaque progression than traditional methods.

## Contribution

The study introduces a novel radiomic model based on pericoronary adipose tissue for predicting rapid plaque progression.

## Key findings

- The PCAT radiomics model outperformed clinical and plaque characteristics models in predicting rapid plaque progression.
- Radiomic features from PCAT improved diagnostic performance with an AUC of up to 0.85 in training data.
- Fibrous plaque volume, diameter stenosis, and fat attenuation index were identified as risk factors for rapid plaque progression.

## Abstract

To explore the value of radiomic features derived from pericoronary adipose tissue (PCAT) obtained by coronary computed tomography angiography for prediction of coronary rapid plaque progression (RPP).

A total of 1233 patients from two centers were included in this multicenter retrospective study. The participants were divided into training, internal validation, and external validation cohorts. Conventional plaque characteristics and radiomic features of PCAT were extracted and analyzed. Random Forest was used to construct five models. Model 1: clinical model. Model 2: plaque characteristics model. Model 3: PCAT radiomics model. Model 4: clinical + radiomics model. Model 5: plaque characteristics + radiomics model. The evaluation of the models encompassed identification accuracy, calibration precision, and clinical applicability. Delong’ test was employed to compare the area under the curve (AUC) of different models.

Seven radiomic features, including two shape features, three first-order features, and two textural features, were selected to build the PCAT radiomics model. In contrast to the clinical model and plaque characteristics model, the PCAT radiomics model (AUC 0.85 for training, 0.84 for internal validation, and 0.81 for external validation; p < 0.05) achieved significantly higher diagnostic performance in predicting RPP. The separate combination of radiomics with clinical and plaque characteristics model did not further improve diagnostic efficacy statistically (p > 0.05).

Radiomic feature analysis derived from PCAT significantly improves the prediction of RPP as compared to clinical and plaque characteristics. Radiomic analysis of PCAT may improve monitoring RPP over time.

Our findings demonstrate PCAT radiomics model exhibited good performance in the prediction of RPP, with potential clinical value.

Rapid plaque progression may be predictable with radiomics from pericoronary adipose tissue.Fibrous plaque volume, diameter stenosis, and fat attenuation index were identified as risk factors for predicting rapid plaque progression.Radiomics features of pericoronary adipose tissue can improve the predictive ability of rapid plaque progression.

Rapid plaque progression may be predictable with radiomics from pericoronary adipose tissue.

Fibrous plaque volume, diameter stenosis, and fat attenuation index were identified as risk factors for predicting rapid plaque progression.

Radiomics features of pericoronary adipose tissue can improve the predictive ability of rapid plaque progression.