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Sajjadieh Khajouei A, Payandeh P, Emami SA, Danesh M. A report of fifty cases with incidental diagnosis of anomalous origin of the right coronary artery from the left sinus of Valsalva. Int J Cardiol 2024; 406:132063. [PMID: 38648913 DOI: 10.1016/j.ijcard.2024.132063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/23/2024] [Accepted: 04/17/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Anomalous origin of the right coronary artery from the left sinus of Valsalva (R-ACAOS) is a relatively rare condition that can potentially lead to devastating outcomes. The current study aims to investigate the cardiac-related disorders among patients with incidental R-ACAOS diagnosis through computed tomography angiography (CTA). METHODS The current cross-sectional study has been conducted on 50 patients diagnosed with R-ACAOS who underwent CTA. Based on CTA, the patients' were categorized as R-ACAOS with interarterial course and non-interarterial course. The demographic and medical characteristics, any history of cardiac intervention and New York Heart Association (NYHA) Functional Classification at the time of diagnosis were recruited. Patients were revisited to assess cardiac-associated variables, including symptoms, the presence of heart failure and current NYHA function class. RESULTS The variables including the history of cardiac intervention (P-value<0.001), the presence of heart failure (P-value = 0.010) and NYHA function class at the time of diagnosis (P-value = 0.006) were remarkably higher among those with interarterial course of R-ACAOS; while, the other variables including chest pain at rest (P-value = 0.55) or on exertion (P-value = 0.12), current NYHA function class, current cardiac-associated symptoms except for dyspnea at rest (P-value = 0.012), mortality and coronary calium score did not differ (P-value>0.05). coronary interventions led to significantly improved NYHA function class (P-value<0.05). CONCLUSION Based on the findings of the current study, R-ACAOS with interarterial course leads to significantly higher rates of atherosclerotic-related symptoms and events compared with the other types of RCA anomalies. Moreover, coronary interventions led to significantly improved NYHA functional class regardless of R-ACAOS category.
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Affiliation(s)
| | - Pedram Payandeh
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sayed Ali Emami
- Heart Failure Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Manizheh Danesh
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.
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Wang Z, Tang C, Zuo R, Zhou A, Xu W, Zhong J, Xu Z, Zhang L. Pre-PCI CT-FFR Predicts Target Vessel Failure After Stent Implantation. J Thorac Imaging 2024; 39:232-240. [PMID: 38800956 DOI: 10.1097/rti.0000000000000791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
OBJECTIVES To investigate the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) before percutaneous coronary intervention (PCI) to predict target vessel failure (TVF) after stent implantation. METHODS This retrospective study included 429 patients (429 vessels) who underwent PCI and stent implantation after CCTA within 3 months. All patients underwent coronary stent implantation between January 2012 and December 2019. A dedicated workstation (Syngo Via, Siemens) was used to analyze and measure the CT-FFR value. The cut-off values of pre-PCI CT-FFR for predicting TVF were defined as 0.80 and the value using the log-rank maximization method, respectively. The primary outcome was TVF, defined as a composite of cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization (TVR), which was a secondary outcome. RESULTS During a median 64.0 months follow-up, the cumulative incidence of TVF was 7.9% (34/429). The cutoff value of pre-PCI CT-FFR based on the log-rank maximization method was 0.74, which was the independent predictor for TVF [hazard ratio (HR): 2.61 (95% CI: 1.13, 6.02); P =0.024] and TVR [HR: 3.63 (95%CI: 1.25, 10.51); P =0.018]. Compared with the clinical risk factor model, pre-PCI CT-FFR significantly improved the reclassification ability for TVF [net reclassification improvement (NRI), 0.424, P <0.001; integrative discrimination index (IDI), 0.011, P =0.022]. Adding stent information to the prediction model resulted in an improvement in reclassification for the TVF (C statistics: 0.711, P =0.001; NRI: 0.494, P <0.001; IDI: 0.020, P =0.028). CONCLUSIONS Pre-PCI CT-FFR ≤0.74 was an independent predictor for TVF or TVR, and integration of clinical, pre-PCI CT-FFR, and stent information models can provide a better risk stratification model in patients with stent implantation.
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Affiliation(s)
- Zewen Wang
- Department of Radiology, Jinling Hospital, Nanjing Medical University
| | - Chunxiang Tang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing
| | - Rui Zuo
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing
| | - Aiming Zhou
- Department of Radiology, Hai'an Hospital of Traditional Chinese Medicine, Nantong, Jiangsu
| | - Wei Xu
- Department of Radiology, Jinling Hospital, Nanjing Medical University
| | - Jian Zhong
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing
| | - Zhihan Xu
- CT Collaboration, Siemens Healthineers, Shanghai, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Nanjing Medical University
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing
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Guo B, Jiang M, Guo X, Tang C, Zhong J, Lu M, Liu C, Zhang X, Qiao H, Zhou F, Xu P, Xue Y, Zheng M, Hou Y, Wang Y, Zhang J, Zhang B, Zhang D, Xu L, Hu X, Zhou C, Li J, Yang Z, Mao X, Lu G, Zhang L. Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD. Sci Bull (Beijing) 2024; 69:1472-1485. [PMID: 38637226 DOI: 10.1016/j.scib.2024.03.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 04/20/2024]
Abstract
Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.
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Affiliation(s)
- Bangjun Guo
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Mengchun Jiang
- Department of Radiology, Affiliated Hospital of Jining Medical University, Jining 272007, China
| | - Xiang Guo
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Chunxiang Tang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Jian Zhong
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Mengjie Lu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Chunyu Liu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Xiaolei Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Hongyan Qiao
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Fan Zhou
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Pengpeng Xu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Yi Xue
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Minwen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an 733399, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110022, China
| | - Yining Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - Jiayin Zhang
- Institute of Diagnostic and Interventional Radiology, and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200235, China
| | - Bo Zhang
- Department of Radiology, Jiangsu Taizhou People's Hospital, Taizhou 225399, China
| | - Daimin Zhang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210012, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - Xiuhua Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China
| | - Changsheng Zhou
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Jianhua Li
- Department of Cardiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Zhiwen Yang
- Shukun (Beijing) Network Technology Co., Ltd., Beijing 102200, China
| | - Xinsheng Mao
- Shukun (Beijing) Network Technology Co., Ltd., Beijing 102200, China
| | - Guangming Lu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China.
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China.
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Zhang XL, Zhang B, Tang CX, Wang YN, Zhang JY, Yu MM, Hou Y, Zheng MW, Zhang DM, Hu XH, Xu L, Liu H, Sun ZY, Zhang LJ. Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study. Eur J Radiol 2023; 168:111133. [PMID: 37827088 DOI: 10.1016/j.ejrad.2023.111133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/11/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
OBJECTIVES To evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms. METHODS The retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model. RESULTS Low-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90-0.96], 0.86 [0.79-0.94], 0.88 [0.83-0.94], and 0.90 [0.84-0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94-0.99], 0.92 [0.86-0.99], 0.92 [0.86-0.99] and 0.94 [0.91-0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone. CONCLUSIONS Comprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.
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Affiliation(s)
- Xiao Lei Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Bo Zhang
- Department of Radiology, Jiangsu Taizhou People's Hospital, Taizhou, Jiangsu 225300, PR China
| | - Chun Xiang Tang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Yi Ning Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Jia Yin Zhang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao tong University Affiliated Sixth People's Hospital, Shanghai 200233, PR China
| | - Meng Meng Yu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao tong University Affiliated Sixth People's Hospital, Shanghai 200233, PR China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110001, PR China
| | - Min Wen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shanxi 710032, PR China
| | - Dai Min Zhang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, PR China
| | - Xiu Hua Hu
- Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang 310006, PR China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 10029, PR China
| | - Hui Liu
- Department of Radiology, Guangdong Province People's Hospital, Guangzhou, Guangdong 510000, PR China
| | - Zhi Yuan Sun
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu 210002, PR China.
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Rudzinski PN, Kruk M, Debski M, Demkow M, Kepka C. Can the Application of Fractional Flow Reserve Computed Tomography in High-risk Patients With Chronic Coronary Syndrome Obviate Downstream Diagnostic Invasive Coronary Procedures? J Thorac Imaging 2023; 38:W77-W78. [PMID: 37423614 DOI: 10.1097/rti.0000000000000727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
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Chen YC, Zhou F, Wang YN, Zhang JY, Yu MM, Hou Y, Xu PP, Zhang XL, Xue Y, Zheng MW, Zhang B, Zhang DM, Hu XH, Xu L, Liu H, Lu GM, Tang CX, Zhang LJ. Optimal Measurement Sites of Coronary-Computed Tomography Angiography-derived Fractional Flow Reserve: The Insight From China CT-FFR Study. J Thorac Imaging 2023; 38:194-202. [PMID: 36469852 DOI: 10.1097/rti.0000000000000687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To investigate the optimal measurement site of coronary-computed tomography angiography-derived fractional flow reserve (FFR CT ) for the assessment of coronary artery disease (CAD) in the whole clinical routine practice. MATERIALS AND METHODS This retrospective multicenter study included 396 CAD patients who underwent coronary-computed tomography angiography, FFR CT , and invasive FFR. FFR CT was measured at 1 cm (FFR CT -1 cm), 2 cm (FFR CT -2 cm), 3 cm (FFR CT -3 cm), and 4 cm (FFR CT -4 cm) distal to coronary stenosis, respectively. FFR CT and invasive FFR ≤0.80 were defined as lesion-specific ischemia. The diagnostic performance of FFR CT to detect ischemia was obtained using invasive FFR as the reference standard. Reduced invasive coronary angiography rate and revascularization efficiency were calculated. After a median follow-up of 35 months in 267 patients for major adverse cardiovascular events (MACE), Cox hazard proportional models were performed with FFR CT values at each measurement site. RESULTS For discriminating lesion-specific ischemia, the areas under the curve of FFR CT -1 cm (0.91) as well as FFR CT -2 cm (0.91) were higher than those of FFR CT -3 cm (0.89) and FFR CT -4 cm (0.88), respectively (all P <0.05). The higher reduced invasive coronary angiography rate (81.6%) was found at FFR CT -1 cm than FFR CT -2 cm (81.6% vs. 62.6%, P <0.05). Revascularization efficiency did not differ between FFR CT -1 cm and FFR CT -2 cm (80.8% vs. 65.5%, P =0.019). In 12.4% (33/267) MACE occurred and only values of FFR CT -2 cm were independently predictive of MACE (hazard ratio: 0.957 [95% CI: 0.925-0.989]; P =0.010). CONCLUSIONS This study indicates FFR CT -2 cm is the optimal measurement site with superior diagnostic performance and independent prognostic role.
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Affiliation(s)
- Yan Chun Chen
- Department of Diagnostic Radiology, Jinling Hospital
| | - Fan Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
| | - Yi Ning Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Jia Yin Zhang
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Institute of Diagnostic and Interventional Radiology, Shanghai
| | - Meng Meng Yu
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Institute of Diagnostic and Interventional Radiology, Shanghai
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang
| | - Peng Peng Xu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
| | - Xiao Lei Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
| | - Yi Xue
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing
| | - Min Wen Zheng
- Department of Radiology, Xijing Hospital, Air Force Military Medical University, Xi'an
| | - Bo Zhang
- Department of Radiology, Taizhou People's Hospital, Taizhou, Jiangsu
| | - Dai Min Zhang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University
| | - Xiu Hua Hu
- Zhejiang University School of Medicine, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing
| | - Hui Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Guang Ming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
| | - Chun Xiang Tang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
| | - Long Jiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
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Zhang LJ, Yang J, Jin Z, Lu GM. Cardiothoracic Imaging in China: Opening Up New Horizons. J Thorac Imaging 2022; 37:353-354. [PMID: 36306266 PMCID: PMC9592163 DOI: 10.1097/rti.0000000000000681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Junjie Yang
- Senior Department of Cardiology, Sixth Medical Center of PLA General Hospital
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Guang Ming Lu
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
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