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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.
Insights
Coronary CT angiography (CCTA)-based radiomics significantly improves the prediction of vessel-specific ischemia compared to traditional quantitative methods. Radiomic features, especially when combined with quantitative data, offer superior diagnostic accuracy for identifying myocardial ischemia.
Area of Science:
- Cardiovascular Imaging
- Radiology
- Artificial Intelligence in Medicine
Background:
- Accurate detection of vessel-specific ischemia is crucial for managing coronary artery disease.
- Coronary CT angiography (CCTA) provides anatomical information, but predicting functional ischemia remains challenging.
- Radiomics, the extraction of quantitative features from medical images, shows potential in enhancing diagnostic capabilities.
Purpose of the Study:
- To evaluate the predictive performance of CCTA-based radiomics for identifying vessel-specific myocardial ischemia.
- To compare radiomic models against quantitative models and CT-derived fractional flow reserve (CT-FFR).
Main Methods:
- Retrospective enrollment of patients who underwent both stress dynamic CT myocardial perfusion imaging (MPI) and CCTA.
- Development and comparison of various logistic regression models using quantitative and radiomic features from CCTA.
- Models included plaque quantitative, lumen quantitative, CT-FFR, plaque radiomic, peri-coronary adipose tissue (pCAT) radiomic, integrative radiomic, and fusion models.
- Ischemia defined by relative myocardial blood flow ≤ 0.75 on stress dynamic CT MPI.
Main Results:
- Radiomic models, particularly the plaque radiomic model (AUC=0.81/0.80), outperformed quantitative models (AUC=0.71/0.68 and 0.69/0.65).
- The integrative radiomic model (AUC=0.83/0.82) showed superior performance to the CT-FFR model (AUC=0.74/0.73).
- The quantitative and radiomic fusion model achieved the highest predictive performance (AUC=0.86/0.84).
Conclusions:
- CCTA-based radiomic features, including plaque and pCAT radiomics, are superior to quantitative features for predicting ischemia.
- Integrating radiomic features with quantitative data significantly enhances the discriminatory value for ischemic identification.
- Radiomics represents a promising tool for non-invasively assessing functional significance of coronary artery stenosis.
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.
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