Scholarlink

Background <p>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.</p> Materials and methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

nnU-Net-based whole-coronary pericoronary adipose tissue radiomics combined with clinical factors for predicting coronary plaque development in individuals with normal coronary arteries

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月18日

查看全文
  • Chao Wang,
  • Bingbing Zhang,
  • Chang Rong,
  • Xiaomin Zheng,
  • Yichao Liu,
  • Xingwang Wu

摘要

Background

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.

Materials and methods

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.

Results

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.

Conclusion

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.