Related Experiment Video
Updated: Jun 18, 2025
In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
Published on: February 16, 2016
Coronary CT Angiography-Based Radiomics to Predict Vessel-Specific Ischemia by Stress Dynamic CT Myocardial Perfusion
Hui Li1, Lan Zhang1, Run-Ze Wang1
1Imaging Center, Harbin Medical University Cancer Hospital, Haping Road No.150, Nangang District, Harbin 150081, China.
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 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.
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.
More Related Videos
05:07Author Spotlight: Improved Localization and Monitoring of Coronary Flow Reserve Using Modified PLAX View in Mice
Published on: August 25, 2023
10:16Retrograde Perfusion and Filling of Mouse Coronary Vasculature as Preparation for Micro Computed Tomography Imaging
Published on: February 10, 2012
