Coronary CT Angiography-Based Radiomics to Predict Vessel-Specific Ischemia by Stress Dynamic CT Myocardial Perfusion Imaging

Abstract

Rationale and ObjectivesTo investigate the predictive value of coronary CT angiography (CCTA)-based radiomics for vessel-specific ischemia by stress dynamic CT myocardial perfusion imaging (MPI). Materials and MethodsPatients with typical angina/atypical angina/non-angina chest pain who underwent both stress dynamic CT MPI and CCTA scans were retrospectively enrolled. The following models were constructed for ischemic prediction using logistic regression and CCTA-derived quantitative and radiomic features: plaque quantitative model, lumen quantitative model, CT-fractional flow reserve (CT-FFR) model, integrative quantitative model, plaque radiomic model, peri-coronary adipose tissue (pCAT) radiomic model, integrative radiomic model, and quantitative and radiomic fusion model. A relative myocardial blood flow ≤ 0.75 on stress dynamic CT MPI was considered ischemic. The models’ performances were quantified by the area under the receiver operating characteristic curve (AUC). Results386 coronary vessels [stenosis grade: 25%~75%; training set: 200 (ischemia/non-ischemia=96/104); test set:186 (ischemia/non-ischemia=79/107)] from 326 patients were included. The plaque radiomic model (training/test set: AUC=0.81/0.80) outperformed (p<.05) both the plaque quantitative (training/test set: AUC=0.71/0.68) model and the lumen quantitative (training/test set: AUC=0.69/0.65) model in identifying ischemia. The integrative radiomic model (training/test set: AUC=0.83/0.82) outperformed (p<.05) the CT-FFR model (training/test set: AUC=0.74/0.73) for ischemic prediction. The quantitative and radiomic fusion model (training/test set: AUC=0.86/0.84) outperformed (p<.05) the integrative quantitative model (training/test set: AUC=0.79/0.77) for ischemic detection. ConclusionThe plaque and pCAT radiomic features were superior to the plaque and pCAT quantitative features in predicting ischemia and the addition of the radiomic features to the quantitative features for ischemic identification yielded incremental discriminatory value.