Peng Peng Xu1, Jian Hua Li2, Fan Zhou1, Meng Di Jiang1, Chang Sheng Zhou1, Meng Jie Lu1, Chun Xiang Tang1, Xiao Lei Zhang1, Liu Yang1, Yuan Xiu Zhang3, Yi Ning Wang4, Jia Yin Zhang5, Meng Meng Yu5, Yang Hou6, Min Wen Zheng7, Bo Zhang8, Dai Min Zhang9, Yan Yi4, Lei Xu10, Xiu Hua Hu11, Hui Liu12, 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 Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. 3. Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, 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, Shaanxi, 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. Sir Run Run Shaw Hospital, Zhejiang University, Zhejiang, 310016, Hangzhou, China. 12. Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou, 510030, 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 image quality of coronary CT angiography (CCTA) on the diagnostic performance of a machine learning-based CT-derived fractional flow reserve (FFRCT). METHODS: This nationwide retrospective study enrolled participants from 10 individual centers across China. FFRCT analysis was performed in 570 vessels in 437 patients. Invasive FFR and FFRCT values ≤ 0.80 were considered ischemia-specific. Four-score subjective assessment based on image quality and objective measurement of vessel enhancement was performed on a per-vessel basis. The effects of body mass index (BMI), sex, heart rate, and coronary calcium score on the diagnostic performance of FFRCT were studied. RESULTS: Among 570 vessels, 216 were considered ischemia-specific by invasive FFR and 198 by FFRCT. Sensitivity and specificity of FFRCT for detecting lesion-specific ischemia were 0.82 and 0.93, respectively. Area under the curve (AUC) of high-quality images (0.93, n = 159) was found to be superior to low-quality images (0.80, n = 92, p = 0.02). Objective image quality and heart rate were also associated with diagnostic performance of FFRCT, whereas there was no statistical difference in diagnostic performance among different BMI, sex, and calcium score groups (all p > 0.05, Bonferroni correction). CONCLUSIONS: This retrospective multicenter study supported the FFRCT as a noninvasive test in evaluating lesion-specific ischemia. Subjective image quality, vessel enhancement, and heart rate affect the diagnostic performance of FFRCT. KEY POINTS: • FFRCTcan be used to evaluate lesion-specific ischemia. • Poor image quality negatively affects the diagnostic performance of FFRCT. • CCTA with ≥ score 3, intracoronary enhancement degree of 300-400 HU, and heart rate below 70 bpm at scanning could be of great benefit to more accurate FFRCTanalysis.
OBJECTIVE: To investigate the effect of image quality of coronary CT angiography (CCTA) on the diagnostic performance of a machine learning-based CT-derived fractional flow reserve (FFRCT). METHODS: This nationwide retrospective study enrolled participants from 10 individual centers across China. FFRCT analysis was performed in 570 vessels in 437 patients. Invasive FFR and FFRCT values ≤ 0.80 were considered ischemia-specific. Four-score subjective assessment based on image quality and objective measurement of vessel enhancement was performed on a per-vessel basis. The effects of body mass index (BMI), sex, heart rate, and coronary calcium score on the diagnostic performance of FFRCT were studied. RESULTS: Among 570 vessels, 216 were considered ischemia-specific by invasive FFR and 198 by FFRCT. Sensitivity and specificity of FFRCT for detecting lesion-specific ischemia were 0.82 and 0.93, respectively. Area under the curve (AUC) of high-quality images (0.93, n = 159) was found to be superior to low-quality images (0.80, n = 92, p = 0.02). Objective image quality and heart rate were also associated with diagnostic performance of FFRCT, whereas there was no statistical difference in diagnostic performance among different BMI, sex, and calcium score groups (all p > 0.05, Bonferroni correction). CONCLUSIONS: This retrospective multicenter study supported the FFRCT as a noninvasive test in evaluating lesion-specific ischemia. Subjective image quality, vessel enhancement, and heart rate affect the diagnostic performance of FFRCT. KEY POINTS: • FFRCTcan be used to evaluate lesion-specific ischemia. • Poor image quality negatively affects the diagnostic performance of FFRCT. • CCTA with ≥ score 3, intracoronary enhancement degree of 300-400 HU, and heart rate below 70 bpm at scanning could be of great benefit to more accurate FFRCTanalysis.
Entities:
Keywords:
Computed tomography angiography; Data accuracy; Fractional flow reserve; Heart rate; Quality control
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: Jonathan James Hyett Bray; Moghees Ahmad Hanif; Mohammad Alradhawi; Jacob Ibbetson; Surinder Singh Dosanjh; Sabrina Lucy Smith; Mahmood Ahmad; Dominic Pimenta Journal: Eur Heart J Open Date: 2022-03-17
Authors: Robin Fabian Gohmann; Patrick Seitz; Konrad Pawelka; Nicolas Majunke; Adrian Schug; Linda Heiser; Katharina Renatus; Steffen Desch; Philipp Lauten; David Holzhey; Thilo Noack; Johannes Wilde; Philipp Kiefer; Christian Krieghoff; Christian Lücke; Sebastian Ebel; Sebastian Gottschling; Michael A Borger; Holger Thiele; Christoph Panknin; Mohamed Abdel-Wahab; Matthias Horn; Matthias Gutberlet Journal: J Clin Med Date: 2022-02-28 Impact factor: 4.241