Abstract
To explore the clinical application value of radiomics model based on pericoronary adipose tissue (PCAT) in predicting coronary heart disease. A retrospective analysis was performed for inpatients who had undergone coronary computed tomography angiography from January to December 2023, and 164 cases of coronary artery lesions were screened as the lesion group and 190 cases of normal coronary artery samples were selected as the control group. The clinical data and imaging data of all patients were collected, the radiomics features were extracted by relevant software, and the "region of interest" of pericoronary fat was delineated, and the selection operator and multivariate logistic regression were used to screen the radiomic features of pericoronary fat. A coronary heart disease evaluation model was constructed by the best radiomics features. Area under the curve values of the PCAT radiomics scoring model for predicting the receiver operating characteristic curve of coronary heart disease were 0.863 and 0.851 in training and test sets, respectively. After calibration curve analysis, PCAT radiomics scoring model has a high consistency between the predictive evaluation results and the actual results of coronary heart disease events. In addition, in the training set, the PCAT radiomics scoring model has a net benefit on all threshold probabilities. In the test set, the model has a negative net return with only a small number of threshold probabilities. After combining the clinical characteristics model, the evaluation accuracy of the model for coronary heart disease can reach 0.896. PCAT radiomics model based on coronary computed tomography angiography can effectively predict and evaluate coronary heart disease, which is of great value for the clinical diagnosis of coronary artery disease.