A combined model integrating nnU-Net-based whole-coronary pericoronary adipose tissue radiomics with clinical factors predicted coronary plaque development with a validation AUC of 0.896.
Cohort (n=210)
Yes
Does a combined model of nnU-Net-based whole-coronary PCAT radiomics and clinical factors predict coronary plaque development in individuals with normal baseline coronary arteries?
An automated nnU-Net-based whole-coronary PCAT radiomics model integrated with clinical factors demonstrates high predictive value for early risk stratification of plaque development in individuals with normal coronary arteries.
Effect estimate: AUC 0.896 (95% CI 0.821-0.970)
Effective early prediction of coronary artery disease remains challenging. This study aims to investigate whether radiomic features of pericoronary adipose tissue (PCAT) derived from the whole-coronary tree using nnU-Net, combined with PCAT metrics and clinical factors, can predict coronary plaque development during follow-up in individuals with normal baseline coronary arteries. In this retrospective study, 210 patients with normal baseline coronary CT angiography (CCTA) findings who underwent follow-up CCTA were included and classified into plaque-positive and plaque-negative groups. Baseline clinical data and PCAT metrics (fat attenuation index FAI and fat volume FV) were collected. Radiomic features were extracted from whole-coronary PCAT based on nnU-Net. Patients were randomly divided into training and validation sets (7:3). Five logistic regression models (clinical, PCAT, clinical–PCAT, radiomics, and combined) were constructed and compared using receiver operating characteristic analysis, calibration curves, and decision curve analysis. During follow-up, 77 of 210 patients (36.7%) developed coronary plaques. One clinical factor, three PCAT metrics, and 20 radiomic features were selected. The combined model demonstrated the best performance, with training/validation set AUC values of 0.931 (95% CI 0.889–0.973) and 0.896 (95% CI 0.821–0.970). Calibration and decision curve analyses demonstrated good agreement and clinical utility. An automated nnU-Net-based whole-coronary PCAT radiomics model, integrated with clinical factors, demonstrates potential for early risk stratification of plaque development in individuals with normal coronary arteries.
Wang et al. (Thu,) conducted a cohort in Normal baseline coronary arteries (n=210). Combined clinical, PCAT, and whole-coronary radiomics model vs. Clinical or PCAT models alone was evaluated on Prediction of coronary plaque development (AUC 0.896, 95% CI 0.821-0.970). A combined model integrating nnU-Net-based whole-coronary pericoronary adipose tissue radiomics with clinical factors predicted coronary plaque development with a validation AUC of 0.896.
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