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Updated: Feb 10, 2026
In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
Published on: February 16, 2016
Development and validation of a computed tomography myocardial perfusion imaging radiomic model for major adverse
Zhiqi Zhong1,2, Dong Li3, Shengliang Liu4
1Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai 200080, China.
Insights
A new combined model using CT myocardial perfusion imaging (CT-MPI) radiomic features significantly improves major adverse cardiovascular events (MACE) prediction in patients with suspected coronary artery disease.
Area of Science:
- Cardiovascular Imaging
- Radiomics
- Medical Artificial Intelligence
Background:
- Accurate prediction of major adverse cardiovascular events (MACE) is vital for risk stratification in patients with suspected coronary artery disease.
- CT myocardial perfusion imaging (CT-MPI) offers detailed perfusion parameters for comprehensive characterization.
Purpose of the Study:
- To develop and validate a combined predictive model for MACE.
- The model integrates clinical risk factors, coronary atherosclerotic characteristics, and radiomic features from CT-MPI.
Main Methods:
- Retrospective enrollment of 784 patients who underwent coronary CT angiography (CCTA) and CT-MPI.
- Radiomic analysis was performed on eight CT-MPI perfusion parameter maps.
- Three models were developed: clinical factors only, clinical factors + myocardial blood flow, and clinical factors + myocardial blood flow + radiomic scores.
Main Results:
- The combined model (Model 3) demonstrated superior MACE prediction performance across training, internal, and external validation sets (C-indices up to 0.898).
- Model 3 significantly outperformed models with only clinical factors or clinical factors plus myocardial blood flow (p < 0.05).
- External validation showed Model 3 achieved high time-dependent AUC values for 1-, 3-, and 5-year MACE prediction (up to 0.890).
Conclusions:
- Radiomic features from multiparametric CT-MPI capture macrovascular and microvascular perfusion abnormalities linked to MACE.
- The developed combined model offers enhanced prognostic performance for MACE prediction compared to conventional approaches.
- The model maintains high interpretability while improving MACE risk stratification.
Aims:
Accurate prediction of major adverse cardiovascular events (MACE) is crucial for risk stratification in patients with suspected coronary artery disease. CT myocardial perfusion imaging (CT-MPI) provides various parameters, which may help comprehensively characterize perfusion features. This study aimed to develop a combined model, including clinical risk factors, coronary atherosclerotic characteristics, and radiomic features derived from CT-MPI, to predict MACE.
Methods And Results:
784 patients who underwent coronary CT angiography (CCTA) and CT-MPI from eight hospitals were retrospectively enrolled. Radiomic analysis was performed on eight perfusion parameter maps. Three prediction models were established accordingly: Model 1 (clinical risk factors and coronary atherosclerotic characteristics), Model 2 (incorporating myocardial blood flow values upon Model 1), and Model 3 (integrating radiomic scores upon Model 2). The C-indices for Model 3 in the training, internal validation, and external validation sets were 0.898 (95% confidence interval [CI]: 0.856-0.947), 0.844 (95% CI: 0.780-0.908), and 0.840 (95% CI: 0.791-0.889), respectively, demonstrating significant improvements over Model 1 and Model 2 (all P < 0.05). In the external validation set, Model 3 had the largest time-dependent areas under the curve (AUC) values for 1-, 3-, and 5-year MACE prediction (0.890 [95% CI: 0.831-0.948], 0.880 [95% CI: 0.823-0.938], and 0.837 [95% CI: 0.726-0.949]), compared with Model 1 and Model 2.
Conclusion:
The radiomic features from multiparametric CT-MPI maps simultaneously captured perfusion features associated with MACE at both macrovascular and microvascular levels. The combined model exhibited improved MACE prognostic performance compared with conventional models while maintaining high interpretability.
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