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- Title
Comparison of machine learning–based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.
- Authors
Guo, Weifeng; Zhao, Shihai; Xu, Haijia; He, Wei; Yin, Lekang; Yao, Zhifeng; Xu, Zhihan; Jin, Hang; Wu, Dong; Li, Chenguang; Yang, Shan; Zeng, Mengsu
- Abstract
Objectives: To compare the diagnostic performance of machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. Methods: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. Results: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). Conclusion: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. Clinical relevance statement: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. Key Points: • Both machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.
- Subjects
CORONARY artery stenosis; MYOCARDIAL perfusion imaging; HEART disease diagnosis; MULTIDETECTOR computed tomography; CARDIAC magnetic resonance imaging
- Publication
European Radiology, 2024, Vol 34, Issue 9, p5654
- ISSN
0938-7994
- Publication type
Article
- DOI
10.1007/s00330-024-10650-6