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

ABSTRACT

RATIONALE AND OBJECTIVES: To 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 METHODS: Patients 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). RESULTS: 386 coronary vessels (stenosis grade 25%∼75%; training set 200 [ischemia/non-ischemia=96/104]; test set186 [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. CONCLUSION: The 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.