AUTHOR=Yang You-Chang , Dou Yang , Wang Zhi-Wei , Yin Ruo-Han , Pan Chang-Jie , Duan Shao-Feng , Tang Xiao-Qiang 

TITLE=Prediction of myocardial ischemia in coronary heart disease patients using a CCTA–Based radiomic nomogram

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=10

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1024773

DOI=10.3389/fcvm.2023.1024773

ISSN=2297-055X

ABSTRACT=<sec><title>Objective</title><p>The present study aimed to predict myocardial ischemia in coronary heart disease (CHD) patients based on the radiologic features of coronary computed tomography angiography (CCTA) combined with clinical factors.</p></sec><sec><title>Methods</title><p>The imaging and clinical data of 110 patients who underwent CCTA scan before DSA or FFR examination in Changzhou Second People’s Hospital, Nanjing Medical University (90 patients), and The First Affiliated Hospital of Soochow University (20 patients) from March 2018 to January 2022 were retrospectively analyzed. According to the digital subtraction angiography (DSA) and fractional flow reserve (FFR) results, all patients were assigned to myocardial ischemia (<italic>n</italic> = 58) and normal myocardial blood supply (<italic>n</italic> = 52) groups. All patients were further categorized into training (<italic>n</italic> = 64) and internal validation (<italic>n</italic> = 26) sets at a ratio of 7:3, and the patients from second site were used as external validation. Clinical indicators of patients were collected, the left ventricular myocardium were segmented from CCTA images using CQK software, and the radiomics features were extracted using pyradiomics software. Independent prediction models and combined prediction models were established. The predictive performance of the model was assessed by calibration curve analysis, receiver operating characteristic (ROC) curve and decision curve analysis.</p></sec><sec><title>Results</title><p>The combined model consisted of one important clinical factor and eight selected radiomic features. The area under the ROC curve (AUC) of radiomic model was 0.826 in training set, and 0.744 in the internal validation set. For the combined model, the AUCs were 0.873, 0.810, 0.800 in the training, internal validation, and external validation sets, respectively. The calibration curves demonstrated that the probability of myocardial ischemia predicted by the combined model was in good agreement with the observed values in both training and validation sets. The decision curve was within the threshold range of 0.1–1, and the clinical value of nomogram was higher than that of clinical model.</p></sec><sec><title>Conclusion</title><p>The radiomic characteristics of CCTA combined with clinical factors have a good clinical value in predicting myocardial ischemia in CHD patients.</p></sec>