Chun Xiang Tang1, Yi Ning Wang2, Fan Zhou1, U Joseph Schoepf3, Marly van Assen4, Robert E Stroud4, Jian Hua Li5, Xiao Lei Zhang1, Meng Jie Lu1, Chang Sheng Zhou1, Dai Min Zhang6, Yan Yi2, Jing Yan7, Guang Ming Lu1, Lei Xu8, Long Jiang Zhang9. 1. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China. 2. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. 3. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Dr, Charleston, SC 29425, United States. 4. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Dr, Charleston, SC 29425, United States. 5. Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China. 6. Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China. 7. Siemens Healthcare Ltd., No. 278 ZhouZhu Road, Shanghai 201318, China. 8. Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 10029, China. Electronic address: leixu2001@hotmail.com. 9. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China. Electronic address: kevinzhlj@163.com.
Abstract
PURPOSE: To evaluate the diagnostic performance of coronary computed tomography angiography derived fractional flow reserve (CT-FFR) with invasive fractional flow reserve (FFR) in patients with coronary artery disease" before "with invasive fractional flow reserve serving as the reference standard. MATERIALS AND METHODS: CT-FFR values based on a machine learning algorithm (cFFRML) in 183 vessels of 136 patients from four centers were measured with invasive FFR as reference standard. The diagnostic performance from our multicenter study was combined into a meta-analysis following a literature search in Web of Science, PubMed, Cochrane library to identify studies comparing diagnostic performance of coronary computed tomography angiography (CCTA) and CT-FFR. Sensitivity, specificity, accuracy were analyzed on both per-vessel and per-patient basis for intermediate lesions and by algorithm. RESULTS: Our multicenter study demonstrated sensitivities, specificities, and accuracies of cFFRML and CCTA of 0.85, 0.94, 0.90, and 0.95, 0.28, 0.55 on a per-vessel basis, respectively. For our meta-analysis, pooled sensitivities, specificities, and accuracies of CT-FFR and CCTA were 0.85, 0.82, 0.82, and 0.85, 0.57, 0.65 with AUC of 0.86 (95%CI: 0.83˜0.89) and 0.83 (95%CI: 0.79˜0.86) on a per-vessel basis, respectively. The sensitivity, specificity and accuracy for intermediate lesions using cFFRML were 0.84, 0.92, and 0.89. No significant difference was found among different algorithms of CT-FFR (P < 0.001). CONSLUSION: This multicenter study with meta-analysis showed that CT-FFR had a high diagnostic accuracy in determining ischemia-specific lesions and intermediate lesions. There was no significant difference when comparing the combined diagnostic performance of different algorithms of CT-FFR with invasive FFR as the reference standard.
PURPOSE: To evaluate the diagnostic performance of coronary computed tomography angiography derived fractional flow reserve (CT-FFR) with invasive fractional flow reserve (FFR) in patients with coronary artery disease" before "with invasive fractional flow reserve serving as the reference standard. MATERIALS AND METHODS: CT-FFR values based on a machine learning algorithm (cFFRML) in 183 vessels of 136 patients from four centers were measured with invasive FFR as reference standard. The diagnostic performance from our multicenter study was combined into a meta-analysis following a literature search in Web of Science, PubMed, Cochrane library to identify studies comparing diagnostic performance of coronary computed tomography angiography (CCTA) and CT-FFR. Sensitivity, specificity, accuracy were analyzed on both per-vessel and per-patient basis for intermediate lesions and by algorithm. RESULTS: Our multicenter study demonstrated sensitivities, specificities, and accuracies of cFFRML and CCTA of 0.85, 0.94, 0.90, and 0.95, 0.28, 0.55 on a per-vessel basis, respectively. For our meta-analysis, pooled sensitivities, specificities, and accuracies of CT-FFR and CCTA were 0.85, 0.82, 0.82, and 0.85, 0.57, 0.65 with AUC of 0.86 (95%CI: 0.83˜0.89) and 0.83 (95%CI: 0.79˜0.86) on a per-vessel basis, respectively. The sensitivity, specificity and accuracy for intermediate lesions using cFFRML were 0.84, 0.92, and 0.89. No significant difference was found among different algorithms of CT-FFR (P < 0.001). CONSLUSION: This multicenter study with meta-analysis showed that CT-FFR had a high diagnostic accuracy in determining ischemia-specific lesions and intermediate lesions. There was no significant difference when comparing the combined diagnostic performance of different algorithms of CT-FFR with invasive FFR as the reference standard.
Authors: Yi Xue; Min Wen Zheng; Yang Hou; Fan Zhou; Jian Hua Li; Yi Ning Wang; Chun Yu Liu; Chang Sheng Zhou; Jia Yin Zhang; Meng Meng Yu; Bo Zhang; Dai Min Zhang; Yan Yi; Lei Xu; Xiu Hua Hu; Guang Ming Lu; Chun Xiang Tang; Long Jiang Zhang Journal: Eur Radiol Date: 2022-01-12 Impact factor: 5.315