Key result
Adding myocardial radiomics to conventional CCTA improves ischemia detection, raising AUC to ~0.79.
Why the study?
The incremental value of myocardial radiomics signature derived from static CCTA for identifying myocardial ischemia based on stress dynamic CT-MPI was unclear.
Does a combined model of CCTA-derived myocardial radiomics and conventional features improve the diagnosis of myocardial ischemia compared to conventional features alone in patients undergoing CT-MPI and CCTA?
Cohort (n=101)
Yes
Does a combined model of CCTA-derived myocardial radiomics and conventional features improve the diagnosis of myocardial ischemia compared to conventional features alone in patients undergoing CT-MPI and CCTA?
Adding a CCTA-derived myocardial radiomics signature to conventional CCTA features significantly improves the diagnostic accuracy for detecting myocardial ischemia.
May support radiomics-augmented CCTA for ischemia detection; leaves open need for prospective validation before practice change.
Objectives: To explore the incremental value of myocardial radiomics signature derived from static coronary computed tomography angiography (CCTA) for identifying myocardial ischemia based on stress dynamic CT myocardial perfusion imaging (CT-MPI). Methods: Patients who underwent CT-MPI and CCTA were retrospectively enrolled from two independent institutions, one used as training and the other as testing. Based on CT-MPI, coronary artery supplying area with relative myocardial blood flow (rMBF) value <0.8 was considered ischemia. Conventional imaging features of target plaques which caused the most severe narrowing of the vessel included area stenosis, lesion length (LL), total plaque burden, calcification burden, non-calcification burden, high-risk plaque (HRP) score, and CT fractional flow reserve (CT-FFR). Myocardial radiomics features were extracted at three vascular supply areas from CCTA images. The optimized radiomics signature was added to the conventional CCTA features to build the combined model (radiomics + conventional). Results: There were 168 vessels from 56 patients enrolled in the training set, and the testing set consisted of 135 vessels from 45 patients. From either cohort, HRP score, LL, stenosis ≥50% and CT-FFR ≤0.80 were associated with ischemia. The optimal myocardial radiomics signature consisted of nine features. The detection of ischemia using the combined model was significantly improved compared with conventional model in both training and testing set (AUC 0.789 vs 0.608, p < 0.001; 0.726 vs 0.637, p = 0.045). Conclusions: Myocardial radiomics signature extracted from static CCTA combining with conventional features could provide incremental value to diagnose specific ischemia. Advances in knowledge: Myocardial radiomics signature extracted from CCTA may capture myocardial characteristics and provide incremental value to detect specific ischemia when combined with conventional features.
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Zhu et al. (2023) conducted a cohort in Myocardial ischemia (n=101). Combined model (myocardial radiomics signature + conventional CCTA features) vs. Conventional CCTA features model was evaluated on Detection of myocardial ischemia (p=<0.001). A combined model of CCTA-derived myocardial radiomics and conventional features significantly improved the detection of myocardial ischemia compared to the conventional model alone (AUC 0.789 vs 0.608, p<0.001).
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