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Zhang X, Zhang B, Zhang F. Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning. Angiology 2024; 75:405-416. [PMID: 37399509 DOI: 10.1177/00033197231187063] [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] [Indexed: 07/05/2023]
Abstract
The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).
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Affiliation(s)
- Xinhong Zhang
- School of Software, Henan University, Kaifeng, China
| | - Boyan Zhang
- School of Software, Henan University, Kaifeng, China
| | - Fan Zhang
- Huaihe Hospital, Henan University, Kaifeng, China
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Yu L, Chen X, Ling R, Yu Y, Yang W, Sun J, Zhang J. Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis. Eur Radiol 2023; 33:2004-2014. [PMID: 36258046 DOI: 10.1007/s00330-022-09175-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/11/2022] [Accepted: 09/18/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate the value of radiomics-based model of pericoronary adipose tissue (PCAT) combined with CT fractional flow reserve (CT-FFR) in predicting hemodynamically significant coronary stenosis. METHODS Patients with suspected or known coronary artery disease, who had coronary computed tomography angiography (CCTA), invasive coronary angiography (ICA), and FFR within 1 month, were retrospectively included. Radiomics features of lesion-based PCAT were extracted. The lesion-specific CT-FFR values, CCTA-derived diameter stenosis, lesion length, and PCAT attenuation were also measured. FFR values were used as the reference standard to assess the diagnostic performance of radiomics model, CT-FFR, and combined model for detection of flow-limiting stenosis. RESULTS A total of 146 patients with 180 lesions were included in the study. All lesions were divided into training and validation cohorts at a ratio of 2:1. CT-FFR model exhibited the highest area under the curve (AUC) (0.803 for training, 0.791 for validation) in predicting hemodynamically significant stenosis, followed by radiomics model (0.776 for training, 0.744 for validation). However, no statistically significant difference was found between the AUCs of the above two models (p > 0.05). When CT-FFR was combined with radiomics model, the AUC reached 0.900 for training cohort and 0.875 for validation cohort, which were significantly higher than that of CT-FFR and radiomics model alone (both p < 0.05). CONCLUSION The diagnostic performance of PCAT radiomics model was comparable to that of CT-FFR for identification of ischemic coronary stenosis. Adding PCAT radiomics model to CT-FFR showed incremental value in discriminating flow-limiting from non-flow-limiting lesions. KEY POINTS • Radiomics analysis of lesion-based PCAT is potentially an alternative method to identify hemodynamic significance of coronary artery stenosis. • Adding radiomics model of PCAT to CT-FFR improved diagnostic performance for the detection of flow-limiting coronary stenosis. • Radiomics features + CT-FFR is a promising noninvasive method for comprehensive evaluation of hemodynamic significance of coronary artery stenosis.
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Affiliation(s)
- Lihua Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China
| | - Xiuyu Chen
- Department of Magnetic Resonance Imaging, 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
| | - Runjianya Ling
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China
| | - Yarong Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China
| | - Wenyi Yang
- Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, China
| | - Jianqing Sun
- Digital Solution, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China.
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Comparison of coronary CT angiography-based and invasive coronary angiography-based quantitative flow ratio for functional assessment of coronary stenosis: A multicenter retrospective analysis. J Cardiovasc Comput Tomogr 2022; 16:509-516. [PMID: 35811245 DOI: 10.1016/j.jcct.2022.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 04/23/2022] [Accepted: 06/20/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND The aim of this study was to evaluate the diagnostic performance of coronary CT angiography (CTA)-based quantitative flow ratio (QFR), namely CT-QFR, and compare it with invasive coronary angiography (ICA)-based Murray law QFR (μQFR), using fractional flow reserve (FFR) as the reference standard. METHODS Patients who underwent coronary CTA, ICA and pressure wire-based FFR assessment within two months were retrospectively analyzed. CT-QFR and μQFR were computed in blinded fashion and compared with FFR, all applying the same cut-off value of ≤0.80 to identify hemodynamically significant stenosis. RESULTS Paired comparison between CT-QFR and μQFR was performed in 191 vessels from 167 patients. Average FFR was 0.81 ± 0.10 and 42.4% vessels had an FFR ≤0.80. CT-QFR had a slightly lower correlation with FFR compared with μQFR, although statistically non-significant (r = 0.87 versus 0.90, p = 0.110). The vessel-level diagnostic performance of CT-QFR was slightly lower but without statistical significance than μQFR (AUC = 0.94 versus 0.97, difference: -0.03 [95%CI: -0.00-0.06], p = 0.095), and substantially higher than diameter stenosis by CTA (AUC difference: 0.17 [95%CI: -0.10-0.23], p < 0.001). The patient-level diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio for CT-QFR to identify FFR value ≤ 0.80 was 88%, 90%, 86%, 86%, 91%, 6.59 and 0.12, respectively. The diagnostic accuracy of CT-QFR was 84% in extensively calcified lesions, while in vessels with no or less calcification, CT-QFR showed a comparable diagnostic accuracy with μQFR (91% versus 92%, p = 0.595). Intra- and inter-observer variability in CT-QFR analysis was -0.00 ± 0.04 and 0.00 ± 0.04, respectively. CONCLUSIONS Performance in diagnosis of hemodynamically significant coronary stenosis by CT-QFR was slightly lower but without statistical significance than μQFR, and substantially higher than CTA-derived diameter stenosis. Extensively calcified lesions reduced the diagnostic accuracy of CT-QFR.
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Jin X, Jin X, Wu X, Chen L, Wang T, Zang W. Distribution of FFRCT in single obstructive coronary stenosis and predictors for major adverse cardiac events: a propensity score matching study. BMC Med Imaging 2022; 22:59. [PMID: 35361151 PMCID: PMC8973531 DOI: 10.1186/s12880-022-00783-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/25/2022] [Indexed: 11/10/2022] Open
Abstract
Background Fractional flow reserve derived from computed tomography (FFRCT) has been demonstrated to improve identification of lesion-specific ischemia significantly compared with coronary computed tomography angiography (CCTA). It remains unclear whether the distribution of FFRCT values in obstructive stenosis between patients who received percutaneous coronary intervention (PCI) or not in routine clinical practice, as well as its association with clinical outcome. This study aims to reveal the distribution of FFRCT value in patients with single obstructive coronary artery stenosis and explored the independent factors for predicting major adverse cardiac events (MACE). Methods This was a retrospective study of adults with non-ST-segment elevation acute coronary syndrome undergoing FFRCT assessment by using CCTA data from January 1, 2016 to December 31, 2020. Propensity score matching (PSM) method was used to account for patient selection bias. The risk factors for predicting MACE were evaluated by a Cox proportional hazards regression analysis. Results Overall, 655 patients with single obstructive (≥ 50%) stenosis shown on CCTA were enrolled and divided into PCI group (279 cases) and conservative group (376 cases) according to treatment strategy. The PSM cohort analysis demonstrated that the difference in history of unstable angina, Canadian Cardiovascular Society Class (CCSC) and FFRCT between PCI group (188 cases) and conservative group (315 cases) was statistically significant, with all P values < 0.05, while the median follow-up time between them was not statistically significant (24 months vs. 22.5 months, P = 0.912). The incidence of MACE in PCI group and conservative group were 14.9% (28/188) and 23.5% (74/315) respectively, P = 0.020. Multivariate analysis of Cox proportional hazards regression revealed that history of unstable angina (adjusted odds ratio (adjOR), 3.165; 95% confidence interval (CI), 2.087–4.800; P < 0.001), FFRCT ≤ 0.8 (OR, 1.632;95% CI 1.095–2.431; P = 0.016), and PCI therapy (OR 0.481; 95% CI 0.305–0.758) were the independent factors for MACE. Conclusions History of unstable angina and FFRCT value of ≤ 0.8 were the independent risk factors for MACE, while PCI therapy was the independent protective factor for MACE.
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Affiliation(s)
- Xianglan Jin
- Department of Cardiac Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, No. 301 Yanchang Middle Road, Shanghai, 200072, China
| | - Xiangyu Jin
- Hainan College of Economics and Business, Haikou, 571127, Hainan, China
| | - Xiaoyun Wu
- Department of Cardiac Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, No. 301 Yanchang Middle Road, Shanghai, 200072, China
| | - Luguang Chen
- Department of Radiology, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, China.
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, China.
| | - Wangfu Zang
- Department of Cardiac Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, No. 301 Yanchang Middle Road, Shanghai, 200072, China.
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Kagiyama N, Tokodi M, Sengupta PP. Machine Learning in Cardiovascular Imaging. Heart Fail Clin 2022; 18:245-258. [DOI: 10.1016/j.hfc.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Dai X, Hou Y, Tang C, Lu Z, Shen C, Zhang L, Zhang J. Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index. Quant Imaging Med Surg 2022; 12:752-765. [PMID: 34993116 DOI: 10.21037/qims-21-424] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/29/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND To investigate the serial changes of computed tomography (CT) fractional flow reserve (CT-FFR) and fat attenuation index (FAI), and explore their relationships with long-term clinical outcomes. METHODS Consecutive symptomatic patients with an intermediate pretest probability of coronary artery disease 1-4 were prospectively enrolled if coronary CT angiography (CCTA) revealed at least 1 lesion with 30-70% stenosis on major epicardial arteries. Follow-up CCTA was performed at 1 to 1.5-year intervals. All patients were further followed up after the second CCTA until September 2019. The Coronary Artery Disease - Reporting and Data System (CAD-RADS) grade, high-risk plaque features, lesion-specific CT-FFR, and FAI were measured for prognosis analysis. RESULTS A total of 263 patients were included in the analysis, and 38 major adverse cardiac events (MACEs) occurred. In the MACE group, the lesion-specific CT-FFR decreased significantly at the follow-up CCTA [0.80 (0.74-0.90) versus 0.85 (0.76-0.93); P=0.01], whereas the FAI did not notably increase (-70.4±8.9 versus -71.3±7.1 HU; P=0.436). In the non-MACE group, lesion-specific CT-FFR increased markedly [0.91 (0.84-0.95) versus 0.90 (0.82-0.94); P<0.001], while the FAI decreased substantially (-74.0±10.8 versus -72.4±11.5 HU; P=0.004). Decreased CT-FFR (adjusted overall hazard ratio =2.455; P=0.023) and increased FAI (adjusted hazard ratio =2.956; P=0.002) were the strongest independent predictors of MACEs. Serial changes of CT-FFR and FAI provided incremental prognostic value (Concordance statistic =0.716; P=0.003; over conventional clinical and imaging parameters (Concordance statistic =0.762; P=0.004). CONCLUSIONS Decreased CT-FFR and increased FAI at follow-up CCTA were the 2 strongest predictors of MACEs. Serial changes of CT-FFR and FAI provided incremental prognostic value over conventional clinical and imaging parameters for risk stratification. In addition, decreased CT-FFR provided incremental predictive value for MACEs from 15 months after second CCTA, while increased FAI added prognostic value from the second CCTA onwards.
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Affiliation(s)
- Xu Dai
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chunxiang Tang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhigang Lu
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Chengxing Shen
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Yu L, Lu Z, Dai X, Shen C, Zhang L, Zhang J. Prognostic value of CT-derived myocardial blood flow, CT fractional flow reserve and high-risk plaque features for predicting major adverse cardiac events. Cardiovasc Diagn Ther 2021; 11:956-966. [PMID: 34527519 DOI: 10.21037/cdt-21-219] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 07/11/2021] [Indexed: 11/06/2022]
Abstract
Background Myocardial blood flow (MBF), CT fractional flow reserve (CT-FFR) and high-risk plaque (HRP) features have been revealed to be associated with patients' prognosis. However, direct intra-individual comparison of these CT-derived parameters has not been explored yet. The aim of this study was to investigate the prognostic value of CT-derived MBF, CT-FFR and HRP features for predicting major adverse cardiac events (MACEs). Methods Consecutive patients with chest pain and intermediate-to-high pre-test probability of coronary artery disease (CAD) were prospectively enrolled. All patients were referred for dynamic CT myocardial perfusion imaging (CT-MPI) + coronary CT angiography (CCTA) and followed up for at least 1 year. MBFischemic (mean MBF of all ischemic segments), MBFratio (MBF of ischemic segments/MBF of reference segments), CT-FFR and HRP features were measured and multivariate analysis was used to evaluate the predictive value of all above parameters for MACEs. Results One hundred and forty-two patients were included into final analysis. MBFischemic and MBFratio was significantly lower in patients with MACE compared to patients without MACE (87 vs. 153 mL/100 mL/min and 0.64 vs. 0.95, both P<0.001). Similarly, CT-FFR was also markedly lower in patients with MACE (0.58 vs. 0.88, P<0.001) whereas coronary artery calcium score (CACS) was significantly higher (1,038.9 vs. 34.2, P<0.001). According to ROC curve analysis, MBFischemic, MBFratio and CACS had largest area under curve (AUC =0.872, 0.855 and 0.813 respectively, all P<0.001) for identifying patients with MACE. After adjusted by multivariate analysis, MBFischemic (hazard ratio =23.382, P=0.003) and CACS (hazard ratio =3.759, P=0.029) were revealed to be the independent predictors for MACE where CT-FFR and HRP features failed to have prognostic value. Conclusions MBFischemic derived from dynamic CT-MPI was the strongest predictor for MACE, followed by CACS. MBFischemic outperformed HRP features and CT-FFR for prediction of unfavorable clinical outcome.
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Affiliation(s)
- Lihua Yu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Zhigang Lu
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xu Dai
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Chengxing Shen
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Lei Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Zuo W, Zhang R, Yang M, Ji Z, He Y, Su Y, Qu Y, Tao Z, Ma G. Clinical prediction models of fractional flow reserve: an exploration of the current evidence and appraisal of model performance. Quant Imaging Med Surg 2021; 11:2642-2657. [PMID: 34079730 DOI: 10.21037/qims-20-1274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background Invasive fractional flow reserve (FFR) is a standard indicator of coronary stenoses' hemodynamic severity. Clinical prediction models (CPMs) may help differentiate ischemic from non-ischemic lesions without using a pressure wire but by integrating related variables. This approach differs from that of physics-based models. However, it is not yet known which CPMs are the most reliable at detecting hemodynamic significance. Methods A systematic review was performed of relevant publications that developed or validated any FFR CPMs from inception to April 2019 in the PubMed, EMBASE, and Cochrane Library databases by two independent authors. The risk of bias and applicability were assessed using the prediction model risk of the bias assessment tool (PROBAST). Results A total of 11 unique CPMs and 5 subsequent external validation studies were identified. The prevalence of hemodynamically significant lesions (FFR ≤0.80) across the studies had a median of 37.1% (range: 20.7-68.0%). Lesion length, percent diameter stenosis, and minimal lumen diameter were the three most frequently used variables in the CPMs. Of the 11 FFR CPMs, 9 (82%) exhibited strong discrimination [area under the curve (AUC) >0.75], and 5 (45%) had been subject to external validation; however, calibration was only available for 3 models (27%). There was a high degree of applicability; however, none of the studies was assessed as having a low risk of bias. A CPM was identified that had undergone rigorous validation and calibration: the DILEMMA score (three validations; median AUC, 0.83). Conclusions Almost half of the existing FFR CPMs had been externally validated. Due to their good discrimination abilities, these FFR CPMs are useful tools that could reduce the need for invasive hemodynamic measurements. Future research that adheres to methodological guidelines should be undertaken to develop high-quality models in this setting. (PROSPERO registration number: CRD42019125011).
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Affiliation(s)
- Wenjie Zuo
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Rui Zhang
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Mingming Yang
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhenjun Ji
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yanru He
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yamin Su
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yangyang Qu
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zaixiao Tao
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Genshan Ma
- Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications. Diagnostics (Basel) 2021; 11:diagnostics11030551. [PMID: 33808677 PMCID: PMC8003459 DOI: 10.3390/diagnostics11030551] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 01/10/2023] Open
Abstract
Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.
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Diagnostic performance of quantitative, semi-quantitative, and visual analysis of dynamic CT myocardial perfusion imaging: a validation study with invasive fractional flow reserve. Eur Radiol 2020; 31:525-534. [PMID: 32794126 DOI: 10.1007/s00330-020-07145-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/24/2020] [Accepted: 08/04/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To investigate the diagnostic performance of absolute myocardial blood flow (MBF), MBFratio, and visual analysis of dynamic CT myocardial perfusion imaging (CT-MPI) for the detection of hemodynamically significant coronary stenosis. METHODS Consecutive patients with chest pain and intermediate-to-high pre-test probability of obstructive coronary artery disease were prospectively enrolled. All patients were referred for dynamic CT-MPI and fractional flow reserve (FFR) measurements within 4 weeks. Absolute MBF, MBFratio (mean MBF of stenosis-subtended territories versus that of reference territories), and visually identified perfusion defect were tested for the diagnostic performance with reference to FFR. RESULTS Sixty-two patients with 95 target vessels were included for final analysis. The mean radiation dose for dynamic CT-MPI was 3.0 (2.2-4.0) mSv. The mean lesion-based absolute MBF value was significantly lower in ischemic segments than that in non-ischemic segments (78.0 (65.0-86.0) mL/min/100 mL vs. 133.0 (117.5-163.8) mL/min/100 mL, p < 0.001). Similarly, the lesion-based MBFratio was also markedly lower in territories with positive FFR results (0.52 (0.44-0.64) vs. 0.93 (0.91-0.97), p < 0.001). According to per-lesion ROC curve analysis, MBF and MBFratio had a similar area under the curve (AUC) for detecting hemodynamically significant lesions (AUC = 0.942 vs. 0.956, p = 0.413), which were larger than that of visual analysis (AUC = 0.802, both p < 0.01). The vessel-based sensitivity, specificity, and diagnostic accuracy were 84.3%, 97.7%, and 90.5% for MBF and 96.1%, 93.2%, and 94.7% for MBFratio. CONCLUSIONS Absolute MBF and MBFratio had similarly excellent diagnostic performance with reference to FFR. In addition, these two parameters outperformed visual analysis for the detection of myocardial ischemia. KEY POINTS • The mean MBF and MBFratio were significantly lower in ischemic segments than those in non-ischemic segments. • Absolute MBF and MBFratio had similar AUCs for the detection of hemodynamically significant lesions (AUC = 0.942 vs. 0.956, p = 0.413), which were larger than that of visual analysis (AUC = 0.802, both p < 0.01). • The vessel-based sensitivity, specificity, and diagnostic accuracy were 84.3%, 97.7%, and 90.5% for absolute MBF and 96.1%, 93.2%, and 94.7% for MBFratio.
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Updates on Fractional Flow Reserve Derived by CT (FFRCT). CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-00816-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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From CT to artificial intelligence for complex assessment of plaque-associated risk. Int J Cardiovasc Imaging 2020; 36:2403-2427. [PMID: 32617720 DOI: 10.1007/s10554-020-01926-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/25/2020] [Indexed: 02/07/2023]
Abstract
The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust information on the overall coronary circulation and luminal stenosis, also providing the ability to assess the composition, morphology, and vulnerability of atherosclerotic plaques. In addition, the perivascular adipose tissue (PVAT) has recently emerged as a marker of increased cardiovascular risk. The addition of PVAT quantification to standard CCTA imaging may provide the ability to extract information on local inflammation, for an individualized approach in coronary risk stratification. The development of image post-processing tools over the past several years allowed CCTA to provide a significant amount of data that can be incorporated into machine learning (ML) applications. ML algorithms that use radiomic features extracted from CCTA are still at an early stage. However, the recent development of artificial intelligence will probably bring major changes in the way we integrate clinical, biological, and imaging information, for a complex risk stratification and individualized therapeutic decision making in patients with CAD. This review aims to present the current evidence on the complex role of CCTA in the detection and quantification of vulnerable plaques and the associated coronary inflammation, also describing the most recent developments in the radiomics-based machine learning approach for complex assessment of plaque-associated risk.
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Yu M, Dai X, Yu L, Lu Z, Shen C, Tao X, Zhang J. Hemodynamic Change of Coronary Atherosclerotic Plaque After Statin Treatment: A Serial Follow-Up Study by Computed Tomography-Derived Fractional Flow Reserve. J Am Heart Assoc 2020; 9:e015772. [PMID: 32384006 PMCID: PMC7660867 DOI: 10.1161/jaha.120.015772] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background Whether statin treatment can improve hemodynamic status of coronary atherosclerotic plaque remains unknown. It is of clinical interest to explore the hemodynamic change of coronary lesions after statin treatment. Methods and Results Consecutive patients with intermediate pre‐test probability of coronary artery disease were prospectively enrolled and underwent baseline coronary computed tomography angiography (CCTA) as well as follow‐up CCTA. The primary end point was to determine the lesion‐specific change of △computed tomography‐derived fractional flow reserve (△CT‐FFR, defined as the change of CT‐FFR value across each lesion) after rosuvastatin treatment. The secondary end point was to compare the change of other plaque characteristics according to serial CCTA findings. 152 patients (mean age: 67.1±9.7 years, 100 men, mean follow‐up duration of 13.9±2.5 months) were finally included. In non‐calcified plaque subgroup, △CT‐FFR was significantly lower at follow‐up compared with baseline (0.051±0.010 versus 0.035±0.012, P=0.013). All other parameters were not found to be significantly different between baseline and follow‐up CCTA measurements. In calcified plaque and mixed plaque subgroups, all parameters showed no significant differences between baseline and follow‐up CCTA groups (P>0.05 for all). According to multivariate regression analysis, non‐calcified plaque was >2 times more likely than calcified plaque to observe the decrease of △CT‐FFR (adjusted hazard ratio: 2.05 [1.03–4.09], P=0.042). Conclusions In patients with mild to intermediate coronary stenosis, rosuvastatin treatment resulted in a reduction in lesion‐specific △CT‐FFR at mid‐term follow‐up. This hemodynamic improvement was mainly observed for non‐calcified lesions.
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Affiliation(s)
- Mengmeng Yu
- Institute of Diagnostic and Interventional Radiology Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai China
| | - Xu Dai
- Institute of Diagnostic and Interventional Radiology Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai China
| | - Lihua Yu
- Institute of Diagnostic and Interventional Radiology Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai China
| | - Zhigang Lu
- Department of Cardiology Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai China
| | - Chengxing Shen
- Department of Cardiology Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai China
| | - Xiaofeng Tao
- Department of Radiology Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jiayin Zhang
- Institute of Diagnostic and Interventional Radiology Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai China
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14
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Baumann S, Hirt M, Rott C, Özdemir GH, Tesche C, Becher T, Weiss C, Hetjens S, Akin I, Schoenberg SO, Borggrefe M, Janssen S, Overhoff D, Lossnitzer D. Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio. J Clin Med 2020; 9:E714. [PMID: 32155743 PMCID: PMC7141220 DOI: 10.3390/jcm9030714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 02/26/2020] [Accepted: 03/03/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFRML) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFRTM) as a novel invasive resting pressure-wire index for detecting hemodynamically significant coronary artery stenosis. METHODS In our single center study, patients with coronary artery disease (CAD) who had a clinically indicated coronary computed tomography angiography (cCTA) and subsequent invasive coronary angiography (ICA) with pressure wire-measurement were included. On-site prototype CT-FFRML software and on-site CT-plaque software were used to calculate the hemodynamic relevance of coronary stenosis. RESULTS We enrolled 33 patients (70% male, mean age 68 ± 12 years). On a per-lesion basis, the area under the receiver operating characteristic curve (AUC) of CT-FFRML (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter4 (LL/MLD4; 0.80), minimal luminal diameter (MLD; 0.77), remodeling index (RI; 0.76), degree of luminal diameter stenosis (0.75), and minimal luminal area (MLA; 0.75). CONCLUSION CT-FFRML and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFRML had the best discriminatory power, using RFRTM as the reference standard.
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Affiliation(s)
- Stefan Baumann
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
| | - Markus Hirt
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
| | - Christina Rott
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
| | - Gökce H. Özdemir
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
| | - Christian Tesche
- Department of Internal Medicine, St. Johannes-Hospital, Dortmund 44137, Germany;
| | - Tobias Becher
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
- Laboratory of Molecular Metabolism, The Rockefeller University, New York NY 10065, USA
| | - Christel Weiss
- Medical Faculty Mannheim, Department of Medical Statistics and Biomathematics, University Medical Center Mannheim, Heidelberg University, Mannheim 68167, Germany; (C.W.); (S.H.)
| | - Svetlana Hetjens
- Medical Faculty Mannheim, Department of Medical Statistics and Biomathematics, University Medical Center Mannheim, Heidelberg University, Mannheim 68167, Germany; (C.W.); (S.H.)
| | - Ibrahim Akin
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
| | - Stefan O. Schoenberg
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Faculty of Medicine Mannheim, Heidelberg University, Mannheim 68167, Germany; (S.O.S.); (S.J.); (D.O.)
| | - Martin Borggrefe
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
| | - Sonja Janssen
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Faculty of Medicine Mannheim, Heidelberg University, Mannheim 68167, Germany; (S.O.S.); (S.J.); (D.O.)
| | - Daniel Overhoff
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Faculty of Medicine Mannheim, Heidelberg University, Mannheim 68167, Germany; (S.O.S.); (S.J.); (D.O.)
| | - Dirk Lossnitzer
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim 68167, Germany; (M.H.); (C.R.); (G.H.Ö.); (T.B.); (I.A.); (M.B.); (D.L.)
- European Center for Angioscience, Mannheim 68167, Germany
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15
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Perivascular fat attenuation index and high-risk plaque features evaluated by coronary CT angiography: relationship with serum inflammatory marker level. Int J Cardiovasc Imaging 2020; 36:723-730. [DOI: 10.1007/s10554-019-01758-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 12/26/2019] [Indexed: 12/17/2022]
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16
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Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Išgum I. Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey. Front Cardiovasc Med 2019; 6:172. [PMID: 32039237 PMCID: PMC6988816 DOI: 10.3389/fcvm.2019.00172] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 11/12/2019] [Indexed: 01/10/2023] Open
Abstract
Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
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Affiliation(s)
- Nils Hampe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jelmer M Wolterink
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sanne G M van Velzen
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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17
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Yu M, Dai X, Deng J, Lu Z, Shen C, Zhang J. Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study. Eur Radiol 2019; 30:673-681. [PMID: 31444596 DOI: 10.1007/s00330-019-06400-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/22/2019] [Accepted: 07/26/2019] [Indexed: 01/22/2023]
Abstract
OBJECTIVE This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions. METHODS Patients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning-based FFRCT, and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant. RESULTS This study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFRCT value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10, p < 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFRCT (AUC, 0.821 vs 0.850; p = 0.426). The diagnostic accuracy of FFRCT was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%, p = 0.093). CONCLUSIONS Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy. KEY POINTS • Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. • Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning-based FFR CTfor predicting ischemic coronary stenosis. • No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features.
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Affiliation(s)
- Mengmeng Yu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yishan Rd, Shanghai, China
| | - Xu Dai
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yishan Rd, Shanghai, China
| | - Jianhong Deng
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yishan Rd, Shanghai, China
| | - Zhigang Lu
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yishan Rd, Shanghai, China
| | - Chengxing Shen
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yishan Rd, Shanghai, China
| | - Jiayin Zhang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yishan Rd, Shanghai, China.
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18
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Yu M, Lu Z, Shen C, Yan J, Wang Y, Lu B, Zhang J. The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR CT, or high-risk plaque features? Eur Radiol 2019; 29:3647-3657. [PMID: 30903334 DOI: 10.1007/s00330-019-06139-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 02/16/2019] [Accepted: 03/07/2019] [Indexed: 01/12/2023]
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.
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Affiliation(s)
- Mengmeng Yu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China
| | - Zhigang Lu
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China
| | - Chengxing Shen
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China
| | - Jing Yan
- Siemens Healthineers, #278, Zhouzhugong Rd, Shanghai, China
| | - Yining Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
| | - Bin Lu
- 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
| | - Jiayin Zhang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China.
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19
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Kaeder F, Rutsch M, Skarga E, Loßnitzer D, Akin I, Baumann S. Diagnostic performance of cCTA derived stenosis predictors to detect hemodynamic significant coronary stenosis. Int J Cardiol 2019; 274:62. [PMID: 30449338 DOI: 10.1016/j.ijcard.2018.07.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 07/09/2018] [Indexed: 11/30/2022]
Affiliation(s)
- Finja Kaeder
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim, Germany
| | - Marlon Rutsch
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim, Germany
| | - Elizaveta Skarga
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim, Germany
| | - Dirk Loßnitzer
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim, Germany
| | - Ibrahim Akin
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim, Germany
| | - Stefan Baumann
- First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany and DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Mannheim, Germany.
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20
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Zhang J. Optimize the comprehensive evaluation of hemodynamical significance by coronary CT angiography. Int J Cardiol 2019; 274:403. [DOI: 10.1016/j.ijcard.2018.08.076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 08/23/2018] [Indexed: 11/16/2022]
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