Published in:
01-07-2019 | Angiography | Cardiac
The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?
Published in: European Radiology | Issue 7/2019
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Objectives
The present study aimed to compare the diagnostic performance of a machine learning (ML)–based FFRCT algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFRICA.
Methods
Patients who underwent both CCTA and FFRICA measurement within 2 weeks were retrospectively included. ML-based FFRCT, volume of subtended myocardium (Vsub), percentage of subtended myocardium volume versus total myocardium volume (Vratio), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFRICA ≤ 0.8 were considered to be functionally significant.
Results
One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, Vsub, Vratio, Vratio/MLD, Vratio/MLA, and LL/MLD4 were all significantly longer or larger in the group of FFRICA ≤ 0.8 while smaller minimal lumen area, MLD, and FFRCT value were noted. The AUC of FFRCT + Vratio/MLD was significantly better than that of FFRCT alone (0.935 versus 0.873, p < 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. Vratio/MLD-complemented ML-based FFRCT for “gray zone” lesions with FFRCT value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208).
Conclusions
ML-based FFRCT simulation and Vratio/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. Vratio/MLD is more accurate than ML-based FFRCT for lesions with simulated FFRCT value from 0.7 to 0.8.
Key Points
• Machine learning–based FFR
CT
and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis.
• Subtended myocardium volume was more accurate than machine learning–based FFR
CT
for “gray zone” lesions with simulated FFR value from 0.7 to 0.8.
• CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.