Meng Di Jiang1, Xiao Lei Zhang1, Hui Liu2, Chun Xiang Tang1, Jian Hua Li3, Yi Ning Wang4, Peng Peng Xu1, Chang Sheng Zhou1, Fan Zhou1, Meng Jie Lu1, Jia Yin Zhang5, Meng Meng Yu5, Yang Hou6, Min Wen Zheng7, Bo Zhang8, Dai Min Zhang9, Yan Yi5, Lei Xu10, Xiu Hua Hu11, Jian Yang12, Guang Ming Lu1, Qian Qian Ni13, Long Jiang Zhang14. 1. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. 2. Department of Radiology, Guangdong General Hospital, Guangzhou, 510080, China. 3. Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. 4. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. 5. Institute of Diagnostic and Interventional Radiology and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China. 6. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110001, China. 7. Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shanxi, China. 8. Department of Radiology, Jiangsu Taizhou People's Hospital, Taizhou, 225300, China. 9. Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China. 10. Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 10029, China. 11. Department of Radiology, Shaoyifu Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, 310016, China. 12. Department of Radiology, the First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China. 13. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. nqqnjumed@hotmail.com. 14. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. kevinzhlj@163.com.
Abstract
OBJECTIVE: To investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)-based coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) with FFR as a reference standard. METHODS: A total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard. RESULTS: Compared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS. CONCLUSION: Coronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification. KEY POINTS: • CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification. • No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes. • No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.
OBJECTIVE: To investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)-based coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) with FFR as a reference standard. METHODS: A total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard. RESULTS: Compared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS. CONCLUSION:Coronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification. KEY POINTS: • CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification. • No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes. • No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.
Entities:
Keywords:
Calcium; Computed tomography angiography; Coronary disease; Data accuracy; Ischemia
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
Authors: Na Zhao; Yang Gao; Bo Xu; Weixian Yang; Lei Song; Tao Jiang; Li Xu; Hongjie Hu; Lin Li; Wenqiang Chen; Dumin Li; Feng Zhang; Lijuan Fan; Bin Lu Journal: Front Cardiovasc Med Date: 2022-01-03