AUTHOR=Huang Ziguang , Chen Jianing , Ding Huan , Pan Haoyan , Xing Zhaoyuan , Zhao Lijuan , Wen Jing , Zhang Zhe , Zhao Baoying , Dai Xu 

TITLE=Radiomics model based on coronary CT angiography for predicting major adverse cardiovascular events in patients with coronary artery disease: comparison of lesion-specific pericoronary adipose tissue model and pericoronary adipose tissue model

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1600942

DOI=10.3389/fcvm.2025.1600942

ISSN=2297-055X

ABSTRACT=ObjectiveTo assess the performance of a lesion-specific pericoronary adipose tissue (PCAT) radiomics model in comparison to a right coronary artery (RCA) PCAT model in predicting major adverse cardiovascular events (MACE) over a three-year period in patients diagnosed with coronary artery disease (CAD). Additionally, the study aims to evaluate the incremental predictive value of combined models integrating clinical features.MethodsThis study conducted a retrospective analysis involving 242 patients with coronary artery disease who underwent coronary CT angiography (CCTA) with MACE occurring in 121 cases. The right coronary artery and lesion-specific PCAT were segmented using the Peri-coronary Adipose Tissue Analysis Tool software (Shukun Technology Co., Ltd.), and 93 radiographic features were extracted, and the features were screened by Pearson correlation coefficients and Lasso regression after the features were processed by Min-Max Normalization. Machine learning techniques were utilized to construct four models: the right coronary artery PCAT model (RCA-model), the lesion-specific PCAT model (LS-model), the clinical model (Cli-model), and two combined models (Cli-RCA model and Cli-LS model). The performance of these models was evaluated by receiver operating characteristic (ROC) curves, calibration curve and decision curve analysis (DCA).ResultsThe LS-model demonstrated superior predictive performance with AUC values of 0.821 and 0.838 in the training and test cohorts, respectively. This performance surpassed that ofthe RCA-model, which recorded AUC values of 0.789 and 0.788. Notably, the Cli-LS model achieved the highest AUCs of 0.873 and 0.877. The difference in AUC was statistically significant (p < 0.05). Calibration curves indicated excellent agreement between predicted and observed risks, as indicated by aHosmer-Lemeshow test result of P > 0.05. Furthermore, decision curve analysis confirmed a higher net clinical benefit.ConclusionLesion-specific PCAT radiomics features demonstrate superior predictive capability for MACE compared to f RCA-based features. Integrating clinical risk factors further enhances model performance, offering a noninvasive imaging tool for risk stratification in patients with CAD.