Mengmeng Yu1, Zhigang Lu2, Chengxing Shen2, Jing Yan3, Yining Wang4, Bin Lu5, Jiayin Zhang6. 1. Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China. 2. Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China. 3. Siemens Healthineers, #278, Zhouzhugong Rd, Shanghai, China. 4. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China. 5. Department of Radiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 6. Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China. andrewssmu@msn.com.
Abstract
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.
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.
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