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Chan C, Wang M, Kong L, Li L, Chi Chan LW. Clinical Applications of Fractional Flow Reserve Derived from Computed Tomography in Coronary Artery Disease. MAYO CLINIC PROCEEDINGS. DIGITAL HEALTH 2025; 3:100187. [PMID: 40206999 PMCID: PMC11975968 DOI: 10.1016/j.mcpdig.2024.100187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/11/2025]
Abstract
Computer tomography-derived fractional flow reserve (CT-FFR) represents a significant advancement in noninvasive cardiac functional assessment. This technology uses computer simulation and anatomical information from computer tomography of coronary angiogram to calculate the CT-FFR value at each point within the coronary vasculature. These values serve as a critical reference for cardiologists in making informed treatment decisions and planning. Emerging evidence suggests that CT-FFR has the potential to enhance the specificity of computer tomography of coronary angiogram, thereby reducing the need for additional diagnostic examinations such as invasive coronary angiography and cardiac magnetic resonance imaging. This could result in savings in financial cost, time, and resources for both patients and health care providers. However, it is important to note that although CT-FFR holds great promise, there are limitations to this technology. Users should be cautious of common pitfalls associated with its use. A comprehensive understanding of these limitations is essential for effectively applying CT-FFR in clinical practice.
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Affiliation(s)
- Cappi Chan
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
| | - Min Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Luoyi Kong
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
| | - Leanne Li
- School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, Zhejiang, China
- Medical Systems Division, FUJIFILM Hong Kong Limited, Tseun Wan, Hong Kong
| | - Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
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Tremamunno G, Pinos D, Zsarnoczay E, Schoepf UJ, Vecsey-Nagy M, Gnasso C, Fink N, Kravchenko D, Hagar MT, Griffith J, O'Doherty J, Laghi A, Emrich T, Varga-Szemes A. Influence of virtual monoenergetic reconstructions on coronary CT angiography-based fractional flow reserve with photon-counting detector CT: intra-individual comparison with energy-integrating detector CT. Insights Imaging 2025; 16:36. [PMID: 39961979 PMCID: PMC11832851 DOI: 10.1186/s13244-025-01927-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Accepted: 02/02/2025] [Indexed: 02/20/2025] Open
Abstract
OBJECTIVES This study aimed to assess the impact of the photon-counting detector (PCD)-CT-based virtual monoenergetic image (VMI) reconstruction keV levels on CT-based fractional flow reserve (CT-FFR), compared to the energy-integrating detector (EID)-CT. METHODS Patients undergoing clinically indicated coronary CT angiography (CCTA) on an EID-CT were prospectively enrolled for a research CCTA on a PCD-CT within 30 days. PCD-CT datasets were reconstructed at VMI levels of 45, 55, 70, and 90 keV. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm by two readers. CT-FFR ≤ 0.80 was considered hemodynamically significant. RESULTS A total of 20 patients (63.3 ± 8.8 years; 13 men (65%) were included. Median CT-FFR values in the per-vessel analysis for PCD-CT scans were 0.86 (0.81-0.92) for 45 keV, 0.87 (0.80-0.93) for 55 keV, 0.85 (0.79-0.92) for 70 keV and 0.82 (0.76-0.89) for 90 keV, and 0.86 (0.71-0.93) for EID-CT. Comparison among different VMIs showed significant differences only for 45 vs. 90 keV (p < 0.001), and 55 vs. 90 keV (p < 0.001). No significant differences were found in the pairwise comparison between any VMI and EID-CT (all p > 0.05). PCD-CT at 70 keV showed the highest correlation (r = 0.83, p < 0.001), agreement (ICC: 0.90 (0.84-0.94)), and the lowest bias (mean bias -0.01; limits of agreement, 0.84/0.94) when compared to EID-CT. CONCLUSION VMI reconstructions showed significant influence on CT-FFR values only at the extreme levels of the spectrum, while no significant differences were found in comparison with EID-CT. VMI at 70 keV demonstrates the highest correlation and agreement, with the lowest bias compared to EID-CT. CRITICAL RELEVANCE STATEMENT Evidence on novel spectral photon-counting detector (PCD)-CT's impact on CT-fractional flow reserve (FFR) is limited; our results demonstrate the feasibility of CT-FFR using PCD-CT, showing no significant differences between various virtual monoenergetic images and energy-integrating detector (EID)-CT values KEY POINTS: The impact of spectral photon-counting detector (PCD)-CT on CT-derived fractional flow reserve (CT-FFR) is unclear. Spectral PCD-CT-based CT-FFR is feasible, differing only at extreme virtual monoenergetic image levels. CT-FFR from PCD-CT at 70 keV showed the strongest correlation with energy-integrating detector-CT.
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Affiliation(s)
- Giuseppe Tremamunno
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Daniel Pinos
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Emese Zsarnoczay
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- MTA-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - U Joseph Schoepf
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Milan Vecsey-Nagy
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Chiara Gnasso
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicola Fink
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Department of Radiology, University Hospital Munich, LMU Munich, Munich, Germany
| | - Dmitrij Kravchenko
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Laboratory Bonn (QILaB), Bonn, Germany
| | - Muhammad Taha Hagar
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Freiburg, Germany
| | - Joseph Griffith
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Jim O'Doherty
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Siemens Medical Solutions, Malvern, PA, USA
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Tilman Emrich
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
| | - Akos Varga-Szemes
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
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Li Z, Xu T, Wang Z, Ding Y, Zhang Y, Lin L, Wang M, Xu L, Zeng Y. Prognostic Significance of Computed Tomography-Derived Fractional Flow Reserve for Long-Term Outcomes in Individuals With Coronary Artery Disease. J Am Heart Assoc 2025; 14:e037988. [PMID: 39791423 PMCID: PMC12054431 DOI: 10.1161/jaha.124.037988] [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: 07/30/2024] [Accepted: 11/15/2024] [Indexed: 01/12/2025]
Abstract
BACKGROUND Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited. METHODS AND RESULTS A retrospective pooled analysis of individual patient data was performed. Deep-learning-based CT-FFR was calculated. All patients enrolled were followed-up for at least 5 years. The primary outcome was major adverse cardiovascular events. The secondary outcome was death or nonfatal myocardial infarction. Predictive abilities for outcomes were compared among 3 models (model 1, constructed using clinical variables; model 2, model 1+coronary computed tomography angiography-derived anatomical parameters; and model 3, model 2+CT-FFR). A total of 2566 patients (median age, 60 [53-65] years; 56.0% men) with coronary artery disease were included. During a median follow-up time of 2197 (2127-2386) days, 237 patients (9.2%) experienced major adverse cardiovascular events. In multivariable-adjusted Cox models, CT-FFR≤0.80 (hazard ratio [HR], 5.05 [95% CI, 3.64-7.01]; P<0.001) exhibited robust predictive value. The discriminant ability was higher in model 2 than in model 1 (Harrell's C-statistics, 0.79 versus 0.64; P<0.001) and was further promoted by adding CT-FFR to model 3 (Harrell's C-statistics, 0.83 versus 0.79; P<0.001). Net reclassification improvement was 0.264 (P<0.001) for model 2 beyond model 1. Of note, compared with model 2, model 3 also exhibited improvement (net reclassification improvement=0.085; P=0.001). As for predicting death or nonfatal myocardial infarction, only incorporating CT-FFR into model 3 showed improved reclassification (net reclassification improvement=0.131; P=0.021). CONCLUSIONS CT-FFR provides strong and incremental prognostic information for predicting long-term outcomes. The combined models incorporating CT-FFR exhibit modest improvement of prediction abilities, which may aid in risk stratification and decision-making.
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Affiliation(s)
- Zhennan Li
- Department of CardiologyBeijing Anzhen Hospital, Capital Medical UniversityBeijingChina
| | - Tingfeng Xu
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of Genomics, Chinese Academy of Sciences and China National Center for BioinformationBeijingChina
| | - Zhiqiang Wang
- Department of CardiologyBeijing Anzhen Hospital, Capital Medical UniversityBeijingChina
| | - Yaodong Ding
- Department of CardiologyBeijing Anzhen Hospital, Capital Medical UniversityBeijingChina
| | - Yang Zhang
- Department of CardiologyBeijing Anzhen Hospital, Capital Medical UniversityBeijingChina
| | - Li Lin
- Department of CardiologyBeijing Anzhen Hospital, Capital Medical UniversityBeijingChina
| | - Minxian Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of Genomics, Chinese Academy of Sciences and China National Center for BioinformationBeijingChina
| | - Lei Xu
- Department of RadiologyBeijing Anzhen Hospital, Capital Medical UniversityBeijingChina
| | - Yong Zeng
- Department of CardiologyBeijing Anzhen Hospital, Capital Medical UniversityBeijingChina
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Hu F, Lai Q, Fang J, He X, Lin C, Hu M, Fan L, Chen L. The impact of transcatheter aortic valve replacement on changes of coronary computed tomography-derived fractional flow reserve. Ann Med 2024; 56:2420860. [PMID: 39466648 PMCID: PMC11520094 DOI: 10.1080/07853890.2024.2420860] [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: 02/07/2024] [Revised: 05/13/2024] [Accepted: 06/15/2024] [Indexed: 10/30/2024] Open
Abstract
BACKGROUND The effect of transcatheter aortic valve replacement (TAVR) on changes of computed tomography-derived fractional flow reserve (CT-FFR) values was controversial. Thus, we aimed to identify the impact of TAVR on changes of CT-FFR values, plaque characteristics, and the associated clinical impact. METHODS This single-center observational study included 39 consecutive patients with severe aortic valve disease undergone TAVR between August 2019 and April 2023, whom were performed with preoperative and postoperative coronary CT angiography (CCTA). The computation of CT-FFR and plaque characteristics was performed by an independent central core laboratory. RESULTS Each patient underwent CCTA and CT-FFR assessment without encountering any complications. Notably, both at discharge and six months post-TAVR, there was a significant improvement observed in the New York Heart Association (NYHA) functional classification, left ventricular fractional shortening, and ejection fraction compared to pre-operative levels. The CT-FFR for left anterior descending artery (LAD), left anterior descending artery (LCX), and right coronary artery (RCA) had no obvious change at discharge compared to pre-operation (0.92 ± 0.05 vs. 0.93 ± 0.05, p = 0.109; 0.96 ± 0.03 vs. 0.95 ± 0.03, p = 0.523; 0.97 ± 0.04 vs. 0.97 ± 0.03, p = 0.533; respectively). Furthermore, TAVR did not exert a significant impact on plaque burden during the perioperative period. Our report suggested that TAVR did not significantly affect coronary CT-FFR measurements and plaque characteristics in the perioperative period, and furthermore, the patients' cardiac function showed gradual improvement in the short-term following discharge.
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Affiliation(s)
- Feng Hu
- Department of Cardiology, Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center, Fuzhou, P. R. China
| | - Qianyao Lai
- Department of Cardiology, Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center, Fuzhou, P. R. China
- School of Health, Fujian Medical University, Fuzhou, P. R. China
| | - Jun Fang
- Department of Cardiology, Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center, Fuzhou, P. R. China
| | - Xi He
- Department of Cardiology, Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center, Fuzhou, P. R. China
| | - Chaoyang Lin
- Department of Cardiology, Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center, Fuzhou, P. R. China
- School of Health, Fujian Medical University, Fuzhou, P. R. China
| | - Mingming Hu
- Pulse Medical Technology Company, Shanghai, P. R. China
| | - Lin Fan
- Department of Cardiology, Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center, Fuzhou, P. R. China
| | - Lianglong Chen
- Department of Cardiology, Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center, Fuzhou, P. R. China
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Magalhães TA, Carneiro ACDC, Moreira VDM, Trad HS, Lopes MMU, Cerci RJ, Nacif MS, Schvartzman PR, Chagas ACP, Costa IBSDS, Schmidt A, Shiozaki AA, Montenegro ST, Piegas LS, Zapparoli M, Nicolau JC, Fernandes F, Hadlich MS, Ghorayeb N, Mesquita ET, Gonçalves LFG, Ramires FJA, Fernandes JDL, Schwartzmann PV, Rassi S, Torreão JA, Mateos JCP, Beck-da-Silva L, Silva MC, Liberato G, Oliveira GMMD, Feitosa Filho GS, Carvalho HDSMD, Markman Filho B, Rocha RPDS, Azevedo Filho CFD, Taratsoutchi F, Coelho-Filho OR, Kalil Filho R, Hajjar LA, Ishikawa WY, Melo CA, Jatene IB, Albuquerque ASD, Rimkus CDM, Silva PSDD, Vieira TDR, Jatene FB, Azevedo GSAAD, Santos RD, Monte GU, Ramires JAF, Bittencourt MS, Avezum A, Silva LSD, Abizaid A, Gottlieb I, Precoma DB, Szarf G, Sousa ACS, Pinto IMF, Medeiros FDM, Caramelli B, Parga Filho JR, Santos TSGD, Prazeres CEED, Lopes MACQ, Avila LFRD, Scanavacca MI, Gowdak LHW, Barberato SH, Nomura CH, Rochitte CE. Cardiovascular Computed Tomography and Magnetic Resonance Imaging Guideline of the Brazilian Society of Cardiology and the Brazilian College of Radiology - 2024. Arq Bras Cardiol 2024; 121:e20240608. [PMID: 39475988 DOI: 10.36660/abc.20240608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2025] Open
Affiliation(s)
- Tiago Augusto Magalhães
- Complexo Hospital de Clínicas da Universidade Federal do Paraná (CHC-UFPR), Curitiba, PR - Brasil
- Hospital do Coração (HCOR), São Paulo, SP - Brasil
- Hospital Sírio Libanês, SP, São Paulo, SP - Brasil
| | | | - Valéria de Melo Moreira
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | | | - Marly Maria Uellendahl Lopes
- Universidade Federal de São Paulo (UNIFESP), São Paulo, SP - Brasil
- DASA - Diagnósticos da América S/A, São Paulo, SP - Brasil
| | | | - Marcelo Souto Nacif
- Universidade Federal Fluminense, Niterói, RJ - Brasil
- Hospital Universitário Antonio Pedro, Niterói, RJ - Brasil
| | | | - Antônio Carlos Palandrini Chagas
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
- Faculdade de Medicina do ABC, Santo André, SP - Brasil
| | | | - André Schmidt
- Universidade de São Paulo (USP), Ribeirão Preto, SP - Brasil
| | - Afonso Akio Shiozaki
- ND Núcleo Diagnóstico, Maringá, PR - Brasil
- Ômega Diagnóstico, Maringá, PR - Brasil
- Hospital Paraná, Maringá, PR - Brasil
| | | | | | - Marcelo Zapparoli
- Quanta Diagnóstico por Imagem, Curitiba, PR - Brasil
- DAPI, Curitiba, PR - Brasil
| | - José Carlos Nicolau
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | - Fabio Fernandes
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | - Marcelo Souza Hadlich
- Fleury Medicina e Saúde, Rio de Janeiro, RJ - Brasil
- Rede D'Or RJ, Rio de Janeiro, RJ - Brasil
- Unimed, Rio de Janeiro, RJ - Brasil
- Instituto Nacional de Cardiologia (INC), Rio de Janeiro, RJ - Brasil
| | - Nabil Ghorayeb
- Instituto Dante Pazzanese de Cardiologia, São Paulo, SP - Brasil
- Inspirali Educação, São Paulo, SP - Brasil
- Anhanguera Educacional, São Paulo, SP - Brasil
| | | | - Luiz Flávio Galvão Gonçalves
- Hospital São Lucas, Rede D'Or SE, Aracaju, SE - Brasil
- Hospital Universitário da Universidade Federal de Sergipe, Aracaju, SE - Brasil
- Clínica Climedi, Aracaju, SE - Brasil
| | - Felix José Alvarez Ramires
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | | | - Pedro Vellosa Schwartzmann
- Hospital Unimed Ribeirão Preto, Ribeirão Preto, SP - Brasil
- Centro Avançado de Pesquisa, Ensino e Diagnóstico (CAPED), Ribeirão Preto, SP - Brasil
| | | | | | - José Carlos Pachón Mateos
- Hospital do Coração (HCOR), São Paulo, SP - Brasil
- Hospital Sírio Libanês, SP, São Paulo, SP - Brasil
| | - Luiz Beck-da-Silva
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS - Brasil
| | | | - Gabriela Liberato
- Hospital Sírio Libanês, SP, São Paulo, SP - Brasil
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | | | | | - Hilka Dos Santos Moraes de Carvalho
- PROCAPE - Universidade de Pernambuco, Recife, PE - Brasil
- Hospital das Clínicas de Pernambuco da Universidade Federal de Pernambuco (UFPE), Recife, PE - Brasil
- Real Hospital Português de Pernambuco, Recife, PE - Brasil
| | - Brivaldo Markman Filho
- Hospital das Clínicas de Pernambuco da Universidade Federal de Pernambuco (UFPE), Recife, PE - Brasil
| | | | | | - Flávio Taratsoutchi
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | | | - Roberto Kalil Filho
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | | | - Walther Yoshiharu Ishikawa
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | - Cíntia Acosta Melo
- Hospital Beneficência Portuguesa de São Paulo, São Paulo, SP - Brasil
- Hospital Infantil Sabará, São Paulo, SP - Brasil
| | | | | | - Carolina de Medeiros Rimkus
- Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
- Instituto D'Or de Pesquisa e Ensino (IDOR), São Paulo SP - Brasil
| | - Paulo Savoia Dias da Silva
- Fleury Medicina e Saúde, Rio de Janeiro, RJ - Brasil
- University of Iowa Hospitals and Clinics, Iowa City - EUA
| | - Thiago Dieb Ristum Vieira
- Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | - Fabio Biscegli Jatene
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | - Guilherme Sant Anna Antunes de Azevedo
- ECOMAX, Blumenau, SC - Brasil
- Hospital Unimed Blumenau, Blumenau, SC - Brasil
- Hospital São José de Jaraguá do Sul, Blumenau, SC - Brasil
- Cliniimagem Criciúma, Blumenau, SC - Brasil
| | - Raul D Santos
- Hospital Sírio Libanês, SP, São Paulo, SP - Brasil
- Universidade de São Paulo (USP), Ribeirão Preto, SP - Brasil
| | | | - José Antonio Franchini Ramires
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | | | - Alvaro Avezum
- Hospital Alemão Oswaldo Cruz, São Paulo, SP - Brasil
| | | | | | - Ilan Gottlieb
- Fonte Imagem Medicina Diagnostica, Rio de Janeiro, RJ - Brasil
| | | | - Gilberto Szarf
- Universidade Federal de São Paulo (UNIFESP), São Paulo, SP - Brasil
| | - Antônio Carlos Sobral Sousa
- Universidade Federal de Sergipe, Aracaju, SE - Brasil
- Hospital São Lucas, Aracaju, SE - Brasil
- Rede D'Or de Aracaju, Aracaju, SE - Brasil
| | | | | | - Bruno Caramelli
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | - José Rodrigues Parga Filho
- Hospital Sírio Libanês, SP, São Paulo, SP - Brasil
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | | | | | | | | | - Mauricio Ibrahim Scanavacca
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
| | - Luis Henrique Wolff Gowdak
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
- Universidade de São Paulo (USP), Ribeirão Preto, SP - Brasil
| | - Silvio Henrique Barberato
- Quanta Diagnóstico por Imagem, Curitiba, PR - Brasil
- Cardioeco, Centro de Diagnóstico Cardiovascular, Curitiba, PR - Brasil
| | | | - Carlos Eduardo Rochitte
- Hospital do Coração (HCOR), São Paulo, SP - Brasil
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo SP - Brasil
- DASA - Diagnósticos da América S/A, São Paulo, SP - Brasil
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6
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Gohmann RF, Schug A, Krieghoff C, Seitz P, Majunke N, Buske M, Kaiser F, Schaudt S, Renatus K, Desch S, Leontyev S, Noack T, Kiefer P, Pawelka K, Lücke C, Abdelhafez A, Ebel S, Borger MA, Thiele H, Panknin C, Abdel-Wahab M, Horn M, Gutberlet M. Interrater Variability of ML-Based CT-FFR in Patients without Obstructive CAD before TAVR: Influence of Image Quality, Coronary Artery Calcifications, and Location of Measurement. J Clin Med 2024; 13:5247. [PMID: 39274460 PMCID: PMC11395889 DOI: 10.3390/jcm13175247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 08/28/2024] [Accepted: 08/29/2024] [Indexed: 09/16/2024] Open
Abstract
Objectives: CT-derived fractional flow reserve (CT-FFR) can improve the specificity of coronary CT-angiography (cCTA) for ruling out relevant coronary artery disease (CAD) prior to transcatheter aortic valve replacement (TAVR). However, little is known about the reproducibility of CT-FFR and the influence of diffuse coronary artery calcifications or segment location. The objective was to assess the reliability of machine-learning (ML)-based CT-FFR prior to TAVR in patients without obstructive CAD and to assess the influence of image quality, coronary artery calcium score (CAC), and the location of measurement within the coronary tree. Methods: Patients assessed for TAVR, without obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers with differing experience. Differences in absolute values and categorization into hemodynamically relevant CAD (CT-FFR ≤ 0.80) were compared. Results in regard to CAD were also compared against invasive coronary angiography. The influence of segment location, image quality, and CAC was evaluated. Results: Of the screened patients, 109/388 patients did not have obstructive CAD on cCTA and were included. The median (interquartile range) difference of CT-FFR values was -0.005 (-0.09 to 0.04) (p = 0.47). Differences were smaller with high values. Recategorizations were more frequent in distal segments. Diagnostic accuracy of CT-FFR between both observers was comparable (proximal: Δ0.2%; distal: Δ0.5%) but was lower in distal segments (proximal: 98.9%/99.1%; distal: 81.1%/81.6%). Image quality and CAC had no clinically relevant influence on CT-FFR. Conclusions: ML-based CT-FFR evaluation of proximal segments was more reliable. Distal segments with CT-FFR values close to the given threshold were prone to recategorization, even if absolute differences between observers were minimal and independent of image quality or CAC.
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Affiliation(s)
- Robin F Gohmann
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Adrian Schug
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Christian Krieghoff
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Patrick Seitz
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Nicolas Majunke
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Maria Buske
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Fyn Kaiser
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Sebastian Schaudt
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Katharina Renatus
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Steffen Desch
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Sergey Leontyev
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Thilo Noack
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Philipp Kiefer
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Konrad Pawelka
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Christian Lücke
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Ahmed Abdelhafez
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Sebastian Ebel
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Michael A Borger
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | | | - Mohamed Abdel-Wahab
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Matthias Horn
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany
| | - Matthias Gutberlet
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
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Ma Z, Tu C, Zhang B, Zhang D, Song X, Zhang H. A meta-analysis comparing the diagnostic performance of computed tomography-derived fractional flow reserve and coronary computed tomography angiography at different levels of coronary artery calcium score. Eur Radiol 2024; 34:5621-5632. [PMID: 38334761 DOI: 10.1007/s00330-024-10591-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/30/2023] [Accepted: 11/30/2023] [Indexed: 02/10/2024]
Abstract
OBJECTIVES The impact of coronary calcification on the diagnostic accuracy of computed tomography-derived fractional flow reserve (CT-FFR) and coronary computed tomography angiography (CCTA) remains a crucial consideration. This meta-analysis aims to compare the diagnostic performance of CT-FFR and CCTA at different levels of coronary artery calcium score (CACS). METHODS AND RESULTS We searched PubMed, Embase, and the Cochrane Library for relevant articles on CCTA, CT-FFR, and invasive fractional flow reserve (FFR). Ten studies were included to evaluate the diagnostic performance of CT-FFR and CCTA at the per-patient and per-vessel levels in four CACS groups. Invasive FFR was used as the reference standard. Except for the CACS ≥ 400 group, the AUC of CT-FFR was higher than those of CCTA in other subgroups of CACS (in CACS < 100 (per-patient, 0.9 (95% CI 0.87-0.92) vs. 0.32 (95% CI 0.28-0.36); per-vessel, 0.92 (95% CI 0.89-0.94) vs. 0.66 (95% CI 0.62-0.7); both p < 0.001), CACS ≥ 100 (per-patient, 0.86 (95% CI 0.82-0.88) vs. 0.44 (95% CI 0.4-0.48); per-vessel, 0.88 (95% CI 0.85-0.9) vs. 0.51 (95% CI 0.46-0.55); both p < 0.001), and CACS < 400 (per-patient, 0.9 (95% CI 0.87-0.93) vs. 0.74 (95% CI 0.7-0.78), p < 0.001; per-vessel, 0.8 (95% CI 0.76-0.83) vs. 0.74 (95% CI 0.7-0.78); p = 0.02)). CONCLUSIONS CT-FFR demonstrates superior diagnostic performance in low CACS groups (CACS < 400) than CCTA in detecting hemodynamic stenoses in patients with coronary artery disease (CAD). CLINICAL RELEVANCE STATEMENT Computed tomography-derived fractional flow reserve might be utilized to determine the necessity of invasive coronary angiography in coronary artery disease patients with coronary artery calcium score < 400. KEY POINTS • There is a lack of meta-analysis comparing the diagnostic performance of computed tomography-derived fractional flow reserve and coronary computed tomography angiography at different levels of calcification. • Computed tomography-derived fractional flow reserve only has a better diagnostic performance than coronary computed tomography angiography with low amounts of coronary calcium. • For the low coronary artery calcium score group, computed tomography-derived fractional flow reserve might be a good non-invasive method to detect hemodynamic stenoses in coronary artery disease patients.
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Affiliation(s)
- Zhao Ma
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People's Republic of China
| | - Chenchen Tu
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People's Republic of China
| | - Baoen Zhang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People's Republic of China
| | - Dongfeng Zhang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People's Republic of China.
| | - Xiantao Song
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People's Republic of China.
| | - Hongjia Zhang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People's Republic of China
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8
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Žuža I, Nadarević T, Jakljević T, Bartolović N, Kovačić S. The Effect of Severe Coronary Calcification on Diagnostic Performance of Computed Tomography-Derived Fractional Flow Reserve Analyses in People with Coronary Artery Disease. Diagnostics (Basel) 2024; 14:1738. [PMID: 39202227 PMCID: PMC11353250 DOI: 10.3390/diagnostics14161738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 08/04/2024] [Accepted: 08/08/2024] [Indexed: 09/03/2024] Open
Abstract
BACKGROUND Negative CCTA can effectively exclude significant CAD, eliminating the need for further noninvasive or invasive testing. However, in the presence of severe CAD, the accuracy declines, thus necessitating additional testing. The aim of our study was to evaluate the diagnostic performance of noninvasive cFFR derived from CCTA, compared to ICA in detecting hemodynamically significant stenoses in participants with high CAC scores (>400). METHODS This study included 37 participants suspected of having CAD who underwent CCTA and ICA. CAC was calculated and cFFR analyses were performed using an on-site machine learning-based algorithm. Diagnostic accuracy parameters of CCTA and cFFR were calculated on a per-vessel level. RESULTS The median total CAC score was 870, with an IQR of 642-1370. Regarding CCTA, sensitivity and specificity for RCA were 60% and 67% with an AUC of 0.639; a LAD of 87% and 50% with an AUC of 0.688; an LCX of 33% and 90% with an AUC of 0.617, respectively. Regarding cFFR, sensitivity and specificity for RCA were 60% and 61% with an AUC of 0.606; a LAD of 75% and 54% with an AUC of 0.647; an LCX of 50% and 77% with an AUC of 0.647. No significant differences between AUCs of coronary CTA and cFFR for each vessel were found. CONCLUSIONS Our results showed poor diagnostic accuracy of CCTA and cFFR in determining significant ischemia-related lesions in participants with high CAC scores when compared to ICA. Based on our results and study limitations we cannot exclude cFFR as a method for determining significant stenoses in people with high CAC. A key issue is accurate and detailed lumen segmentation based on good-quality CCTA images.
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Affiliation(s)
- Iva Žuža
- Department of Diagnostic and Interventional Radiology, Clinical Hospital Centre Rijeka, 51000 Rijeka, Croatia; (T.N.); (N.B.); (S.K.)
| | - Tin Nadarević
- Department of Diagnostic and Interventional Radiology, Clinical Hospital Centre Rijeka, 51000 Rijeka, Croatia; (T.N.); (N.B.); (S.K.)
- Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia;
| | - Tomislav Jakljević
- Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia;
- Clinic for Heart and Vessel Diseases, Clinical Hospital Centre Rijeka, 51000 Rijeka, Croatia
| | - Nina Bartolović
- Department of Diagnostic and Interventional Radiology, Clinical Hospital Centre Rijeka, 51000 Rijeka, Croatia; (T.N.); (N.B.); (S.K.)
- Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia;
| | - Slavica Kovačić
- Department of Diagnostic and Interventional Radiology, Clinical Hospital Centre Rijeka, 51000 Rijeka, Croatia; (T.N.); (N.B.); (S.K.)
- Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia;
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Soschynski M, Storelli R, Birkemeyer C, Hagar MT, Faby S, Schwemmer C, Nous FMA, Pugliese F, Vliegenthart R, Schlett CL, Nikolaou K, Krumm P, Nieman K, Bamberg F, Artzner CP. CT Myocardial Perfusion and CT-FFR versus Invasive FFR for Hemodynamic Relevance of Coronary Artery Disease. Radiology 2024; 312:e233234. [PMID: 39162632 DOI: 10.1148/radiol.233234] [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: 08/21/2024]
Abstract
Background CT-derived fractional flow reserve (CT-FFR) and dynamic CT myocardial perfusion imaging enhance the specificity of coronary CT angiography (CCTA) for ruling out coronary artery disease (CAD). However, evidence on comparative diagnostic value remains scarce. Purpose To compare the diagnostic accuracy of CCTA plus CT-FFR, CCTA plus CT perfusion, and sequential CCTA plus CT-FFR and CT perfusion for detecting hemodynamically relevant CAD with that of invasive angiography. Materials and Methods This secondary analysis of a prospective study included patients with chest pain referred for invasive coronary angiography at nine centers from July 2016 to September 2019. CCTA and CT perfusion were performed with third-generation dual-source CT scanners. CT-FFR was assessed on-site. Independent core laboratories analyzed CCTA alone, CCTA plus CT perfusion, CCTA plus CT-FFR, and a sequential approach involving CCTA plus CT-FFR and CT perfusion for the presence of hemodynamically relevant stenosis. Invasive coronary angiography with invasive fractional flow reserve was the reference standard. Diagnostic accuracy metrics and the area under the receiver operating characteristic curve (AUC) were compared with the Sign test and DeLong test. Results Of the 105 participants (mean age, 64 years ± 8 [SD]; 68 male), 49 (47%) had hemodynamically relevant stenoses at invasive coronary angiography. CCTA plus CT-FFR and CCTA plus CT perfusion showed no evidence of a difference for participant-based sensitivities (90% vs 90%, P > .99), specificities (77% vs 79%, P > .99) and vessel-based AUCs (0.84 [95% CI: 0.77, 0.91] vs 0.83 [95% CI: 0.75, 0.91], P = .90). Both had higher participant-based specificity than CCTA alone (54%, both P < .001) without evidence of a difference in sensitivity between CCTA (94%) and CCTA plus CT perfusion (P = .50) or CCTA plus CT-FFR (P = .63). The sequential approach combining CCTA plus CT-FFR with CT perfusion achieved higher participant-based specificity than CCTA plus CT-FFR (88% vs 77%, P = .03) without evidence of a difference in participant-based sensitivity (88% vs 90%, P > .99) and vessel-based AUC (0.85 [95% CI: 0.77, 0.93], P = .78). Compared with CCTA plus CT perfusion, the sequential approach showed no evidence of a difference in participant-based sensitivity (P > .99), specificity (P = .06), or vessel-based AUC (P = .54). Conclusion There was no evidence of a difference in diagnostic accuracy between CCTA plus CT-FFR and CCTA plus CT perfusion for detecting hemodynamically relevant CAD. A sequential approach combining CCTA plus CT-FFR with CT perfusion led to improved participant-based specificity with no evidence of a difference in sensitivity compared with CCTA plus CT-FFR. ClinicalTrials.gov registration no.: NCT02810795 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Sinitsyn in this issue.
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Affiliation(s)
- Martin Soschynski
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Roberto Storelli
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Clara Birkemeyer
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Muhammad Taha Hagar
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Sebastian Faby
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Chris Schwemmer
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Fay M A Nous
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Francesca Pugliese
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Rozemarijn Vliegenthart
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Christopher L Schlett
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Konstantin Nikolaou
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Patrick Krumm
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Koen Nieman
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Fabian Bamberg
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
| | - Christoph P Artzner
- From the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Strasse 55, 79106 Freiburg im Breisgau, Germany (M.S., R.S., M.T.H., C.L.S., F.B.); Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany (C.B., K. Nikolaou, P.K., C.P.A.); Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany (S.F., C.S.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands (F.M.A.N., K. Nieman); Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts National Institute for Health Research Biomedical Research Centre, Queen Mary University of London, London, United Kingdom (F.P.); Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.V.); and Stanford University School of Medicine and Cardiovascular Institute, Stanford, Calif (K. Nieman)
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10
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Bråten AT, Fossan FE, Muller LO, Jørgensen A, Stensæth KH, Hellevik LR, Wiseth R. Automated computed tomography-derived fractional flow reserve model for diagnosing haemodynamically significant coronary artery disease: a prospective validation study. EUROPEAN HEART JOURNAL. IMAGING METHODS AND PRACTICE 2024; 2:qyae102. [PMID: 39450294 PMCID: PMC11502147 DOI: 10.1093/ehjimp/qyae102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 09/18/2024] [Indexed: 10/26/2024]
Abstract
Aims This study aims to assess the diagnostic performance of a novel computed tomography-derived fractional flow reserve (CT-FFR) algorithm and to compare its accuracy at three predefined sites: (i) at the location of invasive FFR measurements (CT-FFRatloc), (ii) at selected sites determined by an automated module integrated within the algorithm (CT-FFRauto), and (iii) distally in the vessel (CT-FFRdistal). Methods and results We prospectively recruited 108 consecutive patients with stable symptoms of coronary artery disease and at least one suspected obstructive lesion on coronary computed tomography angiography (CCTA). CT-FFR was validated against invasive FFR as gold standard using FFR ≤ 0.80 to define myocardial ischaemia. CT-FFRatloc showed good correlation with invasive FFR (r = 0.67) and improved the ability to detect myocardial ischaemia compared with CCTA at both lesion [area under the curve (AUC) 0.83 vs. 0.65, P < 0.001] and patient level (AUC 0.87 vs. 0.74, P = 0.007). CT-FFRauto demonstrated similar diagnostic accuracy to CT-FFRatloc and significantly improved specificity compared with CT-FFRdistal (86% vs. 49%, P < 0.001). High end CT quality improved the diagnostic performance of CT-FFRauto, demonstrating an AUC of 0.92; similarly, the performance was improved in patients with low-to-intermediate coronary artery calcium score with an AUC of 0.88. Conclusion Implementing an automated module to determine the site of CT-FFR evaluations was feasible, and CT-FFRauto demonstrated comparable diagnostic accuracy to CT-FFRatloc when assessed against invasive FFR. Both CT-FFRatloc and CT-FFRauto improved the diagnostic performance compared with CCTA and improved specificity compared with CT-FFRdistal. High end CT quality and low-to-intermediate calcium burden improved the diagnostic performance of our algorithm. ClinicalTrialsgov Identifier NCT03045601.
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Affiliation(s)
- Anders T Bråten
- Clinic of Cardiology, St. Olavs University Hospital, PO 3250 Torgarden, 7006 Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, PO 8900 Torgarden, 7491 Trondheim, Norway
| | - Fredrik E Fossan
- Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lucas O Muller
- Department of Mathematics, University of Trento, Trento, Italy
| | - Arve Jørgensen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, PO 8900 Torgarden, 7491 Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Knut H Stensæth
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, PO 8900 Torgarden, 7491 Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Leif R Hellevik
- Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - Rune Wiseth
- Clinic of Cardiology, St. Olavs University Hospital, PO 3250 Torgarden, 7006 Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, PO 8900 Torgarden, 7491 Trondheim, Norway
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11
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Zsarnoczay E, Pinos D, Schoepf UJ, Fink N, O'Doherty J, Gnasso C, Griffith J, Vecsey-Nagy M, Suranyi P, Maurovich-Horvat P, Emrich T, Varga-Szemes A. Intra-individual comparison of coronary CT angiography-based FFR between energy-integrating and photon-counting detector CT systems. Int J Cardiol 2024; 399:131684. [PMID: 38151162 DOI: 10.1016/j.ijcard.2023.131684] [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: 10/06/2023] [Revised: 12/12/2023] [Accepted: 12/22/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Coronary computed tomography angiography (CCTA)-based fractional flow reserve (CT-FFR) allows for noninvasive determination of the functional severity of anatomic lesions in patients with coronary artery disease. The aim of this study was to intra-individually compare CT-FFR between photon-counting detector (PCD) and conventional energy-integrating detector (EID) CT systems. METHODS In this single-center prospective study, subjects who underwent clinically indicated CCTA on an EID-CT system were recruited for a research CCTA on PCD-CT within 30 days. Image reconstruction settings were matched as closely as possible between EID-CT (Bv36 kernel, iterative reconstruction strength level 3, slice thickness 0.5 mm) and PCD-CT (Bv36 kernel, quantum iterative reconstruction level 3, virtual monoenergetic level 55 keV, slice thickness 0.6 mm). CT-FFR was measured semi-automatically using a prototype on-site machine learning algorithm by two readers. CT-FFR analysis was performed per-patient and per-vessel, and a CT-FFR ≤ 0.75 was considered hemodynamically significant. RESULTS A total of 22 patients (63.3 ± 9.2 years; 7 women) were included. Median time between EID-CT and PCD-CT was 5.5 days. Comparison of CT-FFR values showed no significant difference and strong agreement between EID-CT and PCD-CT in the per-vessel analysis (0.88 [0.74-0.94] vs. 0.87 [0.76-0.93], P = 0.096, mean bias 0.02, limits of agreement [LoA] -0.14/0.19, r = 0.83, ICC = 0.92), and in the per-patient analysis (0.81 [0.60-0.86] vs. 0.76 [0.64-0.86], P = 0.768, mean bias 0.02, LoA -0.15/0.19, r = 0.90, ICC = 0.93). All included patients were classified into the same category (CT-FFR > 0.75 vs ≤0.75) with both CT systems. CONCLUSIONS CT-FFR evaluation is feasible with PCD-CT and it shows a strong agreement with EID-CT-based evaluation when images are similarly reconstructed.
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Affiliation(s)
- Emese Zsarnoczay
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA; MTA-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Daniel Pinos
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA
| | - Nicola Fink
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA; Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Jim O'Doherty
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA; Siemens Medical Solutions USA Inc, Malvern, USA
| | - Chiara Gnasso
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA; Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Joseph Griffith
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA
| | - Milán Vecsey-Nagy
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA; Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Pal Suranyi
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Tilman Emrich
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA; Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany; German Centre for Cardiovascular Research, Partner site Rhine-Main, Mainz, Germany.
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA
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12
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Zeng Y, Wang X, Tang Z, Li T, Jiang X, Ji F, Zhou Y, Ge J, Li Z, Zhao Y, Ma C, Mintz GS, Nie S. Diagnostic accuracy of CT-FFR with a new coarse-to-fine subpixel algorithm in detecting lesion-specific ischemia: a prospective multicenter study. REVISTA ESPANOLA DE CARDIOLOGIA (ENGLISH ED.) 2024; 77:129-137. [PMID: 37453536 DOI: 10.1016/j.rec.2023.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 06/05/2023] [Indexed: 07/18/2023]
Abstract
INTRODUCTION AND OBJECTIVES A new computed tomography-derived fractional flow reserve (CT-FFR) technique with a "coarse-to-fine subpixel" algorithm has been developed to generate precise lumen contours. The aim of this study was to assess the diagnostic performance of this new CT-FFR algorithm for discriminating lesion-specific ischemia using wire-based FFR ≤ 0.80 as the reference standard in patients with coronary artery disease. METHODS This prospective, multicenter study screened 330 patients undergoing coronary CT angiography (CCTA) and invasive FFR (median interval 2 days) from 6 tertiary hospitals. CT-FFR was evaluated in a blinded fashion with a "coarse-to-fine subpixel" algorithm for lumen contour. RESULTS Between March 2019 and May 2020, we included 316 patients with 324 vessels. There was a good correlation between CT-FFR and invasive FFR (r=0.76, P<.001). The diagnostic sensitivity, specificity, and accuracy on a per-vessel level were 95.3%, 89.8%, and 92.0% for CT-FFR, and 96.4%, 26.4%, and 53.1% for CCTA>50% stenosis, respectively. CT-FFR showed improved discrimination of ischemia compared with CCTA alone overall (AUC, 0.95 vs 0.74, P<.001) and in intermediate (AUC, 0.96 vs 0.62, P<.001) and "gray zone" lesions (AUC, 0.88 vs 0.61, P<.001). The diagnostic specificity, accuracy, and AUC for CT-FFR (71.9%, 82.8%, and 0.84) outperformed CCTA (9.4%, 48.3%, and 0.66) in patients or in vessels with severe calcification (all P<.05). CONCLUSIONS CT-FFR with a new "coarse-to-fine subpixel" algorithm showed high performance in identifying hemodynamically significant stenosis. The diagnostic performance of CT-FFR was superior to that of CCTA in intermediate lesions, "gray zone" lesions, and severely calcified lesions. Clinical Trial Register: NCT04731285.
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Affiliation(s)
- Yaping Zeng
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Xiao Wang
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Zhe Tang
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Tianchang Li
- Department of Cardiology, Sixth Medical Center of PLA General Hospital, Beijing, China
| | - Xuejun Jiang
- Department of Cardiology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Fusui Ji
- Department of Cardiology, Beijing Hospital, Beijing, China
| | - Yujie Zhou
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Junbo Ge
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhanquan Li
- Department of Cardiology, Liaoning Provincial People's Hospital, Shenyang, China
| | - Yanyan Zhao
- Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fu Wai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Changsheng Ma
- Arrhythmia Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Gary S Mintz
- Cardiovascular Research Foundation, New York, United States
| | - Shaoping Nie
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
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Xia J, Bachour K, Suleiman ARM, Roberts JS, Sayed S, Cho GW. Enhancing coronary artery plaque analysis via artificial intelligence-driven cardiovascular computed tomography. Ther Adv Cardiovasc Dis 2024; 18:17539447241303399. [PMID: 39625215 PMCID: PMC11615974 DOI: 10.1177/17539447241303399] [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: 02/26/2024] [Accepted: 11/12/2024] [Indexed: 12/06/2024] Open
Abstract
Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality of cardiac structures and vasculature considered comparable to invasive coronary angiography for the evaluation of coronary artery disease (CAD) in several major cardiovascular guidelines. Conventional image acquisition, processing, and analysis of CCTA imaging have progressed significantly in the past decade through advances in technology, computation, and engineering. However, the advent of artificial intelligence (AI)-driven analysis of CCTA further drives past the limitations of conventional CCTA, allowing for greater achievements in speed, consistency, accuracy, and safety. AI-driven CCTA (AI-CCTA) has achieved a significant reduction in radiation exposure for patients, allowing for high-quality scans with sub-millisievert radiation doses. AI-CCTA has demonstrated comparable accuracy and consistency in manual coronary artery calcium scoring against expert human readers. An advantage over invasive coronary angiography, which provides luminal information only, CCTA allows for plaque characterization, providing detailed information on the quality of plaque and offering further prognosticative value for the management of CAD. Combined with AI, many recent studies demonstrate the efficacy, accuracy, efficiency, and precision of AI-driven analysis of CCTA imaging for the evaluation of CAD, including assessing degree stenosis, adverse plaque characteristics, and CT fractional flow reserve. The limitations of AI-CCTA include its early phase in investigation, the need for further improvements in AI modeling, possible medicolegal implications, and the need for further large-scale validation studies. Despite these limitations, AI-CCTA represents an important opportunity for improving cardiovascular care in an increasingly advanced and data-driven world of modern medicine.
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Affiliation(s)
- Jeffrey Xia
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Kinan Bachour
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | | | - Sammy Sayed
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Geoffrey W. Cho
- David Geffen School of Medicine at UCLA, 100 Medical Plaza, Suite 545, Los Angeles, CA 90024, USA
- Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA
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14
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Gohmann RF, Schug A, Pawelka K, Seitz P, Majunke N, El Hadi H, Heiser L, Renatus K, Desch S, Leontyev S, Noack T, Kiefer P, Krieghoff C, Lücke C, Ebel S, Borger MA, Thiele H, Panknin C, Abdel-Wahab M, Horn M, Gutberlet M. Interrater variability of ML-based CT-FFR during TAVR-planning: influence of image quality and coronary artery calcifications. Front Cardiovasc Med 2023; 10:1301619. [PMID: 38188259 PMCID: PMC10768187 DOI: 10.3389/fcvm.2023.1301619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/13/2023] [Indexed: 01/09/2024] Open
Abstract
Objective To compare machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) in patients before transcatheter aortic valve replacement (TAVR) by observers with differing training and to assess influencing factors. Background Coronary computed tomography angiography (cCTA) can effectively exclude CAD, e.g. prior to TAVR, but remains limited by its specificity. CT-FFR may mitigate this limitation also in patients prior to TAVR. While a high reliability of CT-FFR is presumed, little is known about the reproducibility of ML-based CT-FFR. Methods Consecutive patients with obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers. Categorization into hemodynamically significant CAD was compared against invasive coronary angiography. The influence of image quality and coronary artery calcium score (CAC) was examined. Results CT-FFR was successfully performed on 214/272 examinations by both observers. The median difference of CT-FFR between both observers was -0.05(-0.12-0.02) (p < 0.001). Differences showed an inverse correlation to the absolute CT-FFR values. Categorization into CAD was different in 37/214 examinations, resulting in net recategorization of Δ13 (13/214) examinations and a difference in accuracy of Δ6.1%. On patient level, correlation of absolute and categorized values was substantial (0.567 and 0.570, p < 0.001). Categorization into CAD showed no correlation to image quality or CAC (p > 0.13). Conclusion Differences between CT-FFR values increased in values below the cut-off, having little clinical impact. Categorization into CAD differed in several patients, but ultimately only had a moderate influence on diagnostic accuracy. This was independent of image quality or CAC.
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Affiliation(s)
- Robin F. Gohmann
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
- Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Adrian Schug
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
- Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Konrad Pawelka
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
- Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Patrick Seitz
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
| | - Nicolas Majunke
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Hamza El Hadi
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Linda Heiser
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
| | - Katharina Renatus
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
- Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Steffen Desch
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Sergey Leontyev
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Thilo Noack
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Philipp Kiefer
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | | | | | - Sebastian Ebel
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
- Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Michael A. Borger
- Department of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
- Helios Health Institute, Leipzig, Germany
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
- Helios Health Institute, Leipzig, Germany
| | | | - Mohamed Abdel-Wahab
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Matthias Horn
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Matthias Gutberlet
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
- Medical Faculty, University of Leipzig, Leipzig, Germany
- Helios Health Institute, Leipzig, Germany
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15
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Aromiwura AA, Settle T, Umer M, Joshi J, Shotwell M, Mattumpuram J, Vorla M, Sztukowska M, Contractor S, Amini A, Kalra DK. Artificial intelligence in cardiac computed tomography. Prog Cardiovasc Dis 2023; 81:54-77. [PMID: 37689230 DOI: 10.1016/j.pcad.2023.09.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/11/2023]
Abstract
Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.
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Affiliation(s)
| | - Tyler Settle
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Muhammad Umer
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jonathan Joshi
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Matthew Shotwell
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jishanth Mattumpuram
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Mounica Vorla
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Maryta Sztukowska
- Clinical Trials Unit, University of Louisville, Louisville, KY, USA; University of Information Technology and Management, Rzeszow, Poland
| | - Sohail Contractor
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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16
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Giannopoulos AA, Keller L, Sepulcri D, Boehm R, Garefa C, Venugopal P, Mitra J, Ghose S, Deak P, Pack JD, Davis CL, Stähli BE, Stehli J, Pazhenkottil AP, Kaufmann PA, Buechel RR. High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard. AJR Am J Roentgenol 2023; 221:460-470. [PMID: 37132550 DOI: 10.2214/ajr.23.29156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.
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Affiliation(s)
- Andreas A Giannopoulos
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
| | - Lukas Keller
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
| | - Daniel Sepulcri
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
| | - Reto Boehm
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
| | - Chrysoula Garefa
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
| | | | | | | | | | | | | | - Barbara E Stähli
- Department of Cardiology, University Heart Center, University Hospital Zurich, Zurich, Switzerland
| | - Julia Stehli
- Department of Cardiology, University Heart Center, University Hospital Zurich, Zurich, Switzerland
| | - Aju P Pazhenkottil
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
| | - Ronny R Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland
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Shan D, Ding Y, Wang X, Liu Z, Dou G, Wang K, Zhang W, Jing J, He B, Li Y, Yang J, Chen Y. Incremental diagnostic value of perivascular fat attenuation index for identifying hemodynamically significant ischemia with severe calcification. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2023; 39:1323-1332. [PMID: 36961598 DOI: 10.1007/s10554-023-02831-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/03/2023] [Indexed: 03/25/2023]
Abstract
PURPOSE To explore the incremental value of perivascular fat attenuation index (FAI) to identify hemodynamically significant ischemia in severe calcified vessels. METHODS Patients who underwent coronary computed tomographic angiography (CCTA) examination at Chinese PLA General Hospital from 2017 to 2020 and subsequently underwent fractional flow reserve (FFR) examination within 1 month were consecutively included. Several CCTA-derived indices were measured, including the coronary artery calcification score (CACS), lesion length, ≥CAD-RADS 4 proportion, perivascular FAI and CT-FFR. The included vessels were divided into a nonsevere calcification group and a severe calcification group according to the quartile of CACS. FFR ≤ 0.80 represents the presence of hemodynamically significant ischemia. RESULTS A total of 124 patients with 152 vessels were included (age: 61.1 ± 9.2 years; male 64.5%). Significant differences in lesion length (28.4 ± 14.2 vs. 23.1 ± 12.3 mm, P = 0.021), perivascular FAI (-73.0 ± 7.5 vs. -79.0 ± 7.4 HU, P < 0.001) and CT-FFR (0.78 ± 0.06 vs. 0.86 ± 0.04, P < 0.001) were noted between the FFR ≤ 0.80 group (47 vessels) and the FFR > 0.80 group (105 vessels). Furthermore, the perivascular FAI in the FFR ≤ 0.80 group was significantly greater than that in the FFR > 0.80 group (nonsevere calcification: -73.2 ± 7.5 vs. -78.2 ± 7.4 HU, P = 0.002; severe calcification: -72.8 ± 7.7 vs. -82.7 ± 6.3 HU, P < 0.001). In discriminating hemodynamically significant ischemia, the specificity and accuracy of CT-FFR were significantly affected by severe calcification, which demonstrated a significantly declining trend (P = 0.033 and P = 0.010, respectively). The diagnostic performance of CT-FFR in the severe calcification group was lower than that in the nonsevere calcified group. However, perivascular FAI showed good discriminative performance in the severe calcification group. In combination with perivascular FAI, the predictive value of CT-FFR in identifying hemodynamically significant ischemia with severe calcification increased from an AUC of 0.740 to 0.919. CONCLUSION For coronary artery with severe calcification, the diagnostic performance of CT-FFR in discriminating flow-limiting lesions could be greatly impaired. Perivascular FAI represents a potential reliable imaging marker to provide incremental diagnostic value over CT-FFR for identifying hemodynamically significant ischemia with severe calcification.
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Affiliation(s)
- Dongkai Shan
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China
| | - Yipu Ding
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Xi Wang
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China
| | - Zinuan Liu
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Guanhua Dou
- Department of Cardiology, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Kai Wang
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Wei Zhang
- Department of Cardiology, the First Medical Center of PLA General Hospital, Beijing, China
| | - Jing Jing
- Department of Cardiology, the First Medical Center of PLA General Hospital, Beijing, China
| | - Bai He
- Department of Cardiology, the First Medical Center of PLA General Hospital, Beijing, China
| | - Yang Li
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China
| | - Junjie Yang
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
| | - Yundai Chen
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, 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: 14] [Impact Index Per Article: 7.0] [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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Zhang LJ, Tang C, Xu P, Guo B, Zhou F, Xue Y, Zhang J, Zheng M, Xu L, Hou Y, Lu B, Guo Y, Cheng J, Liang C, Song B, Zhang H, Hong N, Wang P, Chen M, Xu K, Liu S, Jin Z, Lu G. Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology. J Thorac Imaging 2022; 37:385-400. [PMID: 36162081 DOI: 10.1097/rti.0000000000000679] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.
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Affiliation(s)
- Long Jiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Chunxiang Tang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Pengpeng Xu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Bangjun Guo
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Fan Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Yi Xue
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
| | - Minwen Zheng
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University-Xi'an
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University
| | - Bin Lu
- Department of Radiology, State Key Laboratory and National Center for Cardiovascular Diseases, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing
| | - Youmin Guo
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province
| | - Bin Song
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital
| | - Peijun Wang
- Department of Radiology, Tongji Hospital of Tongji University School of Medicine
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology
| | - Ke Xu
- Department of Interventional Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province
| | - Shiyuan Liu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences
| | - Zhengyu Jin
- Department of Medical Imaging and Nuclear Medicine, Changzheng Hospital of Naval Medical University, Shanghai
| | - Guangming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
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You D, Yu H, Wang Z, Wei X, Wu X, Pan C. The correlation of pericoronary adipose tissue with coronary artery disease and left ventricular function. BMC Cardiovasc Disord 2022; 22:398. [PMID: 36068548 PMCID: PMC9446702 DOI: 10.1186/s12872-022-02843-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/31/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE We sought to investigate the correlation of pericoronary adipose tissue with coronary artery disease and left ventricular (LV) function. METHODS Participants with clinically suspected coronary artery disease were enrolled. All participants underwent coronary computed tomography angiography (CCTA) and echocardiography followed by invasive coronary angiography (ICA) within 6 months. Pericoronary adipose tissue (PCAT) was extracted to analyze the correlation with the Gensini score and LV function parameters, including IVS, LVPW, LVEDD, LVESD, LVEDV, LVESV, FS, LVEF, LVM, and LVMI. The correlation between PCAT and the Gensini score was assessed using Spearman's correlation analysis, and that between the PCAT volume or FAI and LV function parameters was determined using partial correlation analysis. RESULTS One hundred and fifty-nine participants (mean age, 64.55 ± 10.64 years; men, 65.4% [104/159]) were included in the final analysis. Risk factors for coronary artery disease, such as hypertension, diabetes, dyslipidemia, and a history of smoking or drinking, had no significant association with PCAT (P > 0.05), and there was also no correlation between PCAT and the Gensini score. However, the LAD-FAI was positively correlated with the IVS (r = 0.203, P = 0.013), LVPW (r = 0.218, P = 0.008), LVEDD (r = 0.317, P < 0.001), LVESD (r = 0.298, P < 0.001), LVEDV (r = 0.317, P < 0.001), LVESV (r = 0.301, P < 0.001), LVM (r = 0.371, P < 0.001), and LVMI (r = 0.304, P < 0.001). Also, the LCX-FAI was positively correlated with the LVEDD (r = 0.199, P = 0.015), LVESD (r = 0.190, P = 0.021), LVEDV (r = 0.203, P = 0.013), LVESV (r = 0.197, P = 0.016), LVM (r = 0.220, P = 0.007), and LVMI (r = 0.172, P = 0.036), and the RCA-FAI was positively correlated with the LVEDD (r = 0.258, P = 0.002), LVESD (r = 0.238, P = 0.004), LVEDV (r = 0.266, P = 0.001), LVESV (r = 0.249, P = 0.002), LVM (r = 0.237, P = 0.004), and LVMI (r = 0.218, P = 0.008), respectively. Finally, the total volume was positively correlated with FS (r = 0.167, P = 0.042). CONCLUSION The FAI was positively correlated with the LV function but was not associated with the severity of coronary artery disease.
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Affiliation(s)
- Deshu You
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, 213003, Jiangsu, China
| | - Haiyang Yu
- Department of Interventional and Vascular Surgery, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, 213003, China
| | - Zhiwei Wang
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, 213003, Jiangsu, China
| | - Xiaoyu Wei
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, 213003, Jiangsu, China
| | - Xiangxiang Wu
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, 213003, Jiangsu, China
| | - Changjie Pan
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, 213003, Jiangsu, China.
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21
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Aditya CR, Sattaru NC, Gopal K, Rahul R, Chandra Shekara G, Nasif O, Alharbi SA, Raghavan SS, Jayadhas SA. Machine Learning Approach for Cardiovascular Risk and Coronary Artery Calcification Score. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2632770. [PMID: 35782065 PMCID: PMC9246606 DOI: 10.1155/2022/2632770] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/27/2022] [Accepted: 05/06/2022] [Indexed: 11/30/2022]
Abstract
Coronary artery calcification (CAC) could assist in the discovery of new risk elements for coronary artery disorder. CAC evaluation, on the other hand, is difficult due to the wide range of CAC in the populations. As a reason, evaluating and analysing data among research have become complicated. In the Research of Inherited Risk Factors for Coronary Atherosclerosis, we used CAC information to test the effects of different analytical methodologies on the correlation with recognized cardiovascular risk elements in asymptomatic patients. Cardiac computed tomography (CT) is also seeing an increase in examinations, and machine learning (ML) could assist with the growing amount of extracted data. Furthermore, there are other sectors in cardiac CT where machine learning could be crucial, including coronary calcium scoring, perfusion, and CT angiography. The establishment of risk evaluation algorithms based on information from CAC utilizing machine learning could assist in the categorization of patients undergoing cardiovascular into distinct risk groups and effectively adapt their treatments to their unique situations. Our findings imply that for forecasting CVD occurrences in asymptomatic people, age-sex segmentation by CAC percentile rank is as effective as absolute CAC scoring. Longitudinal population-based investigations are currently underway and would offer further definitive findings. While machine learning is a strong technology with a lot of possibilities, its implementations in the domain of cardiac CAC are generally in the early stages of development and are not currently commonly accessible in medical practise because of the requirement for substantial verification. Enhanced machine learning will, however, have a significant effect on cardiovascular and coronary artery calcification in the upcoming years.
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Affiliation(s)
- C. R. Aditya
- Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka 570002, India
| | | | - Kumaraguruparan Gopal
- Department of Physiotherapy, College of Health Sciences, Gulf Medical University, Ajman 4184, UAE
| | - R. Rahul
- Department of Mathematics, BMS College of Engineering, Bengaluru, Karnataka 560019, India
| | - G. Chandra Shekara
- Department of Mathematics, BMS College of Engineering, Bengaluru, Karnataka 560019, India
| | - Omaima Nasif
- Department of Physiology, College of Medicine and King Khalid University Hospital, King Saud University, Medical City, PO Box 2925, Riyadh 11461, Saudi Arabia
| | - Sulaiman Ali Alharbi
- Department of Botany and Microbiology, College of Science, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia
| | - S. S. Raghavan
- Department of Microbiology, University of Texas Health and Science Center at Tyler, Tyler 75703, TX, USA
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22
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Liao J, Huang L, Qu M, Chen B, Wang G. Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects. Front Cardiovasc Med 2022; 9:896366. [PMID: 35783834 PMCID: PMC9247240 DOI: 10.3389/fcvm.2022.896366] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/18/2022] [Indexed: 12/28/2022] Open
Abstract
Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.
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Affiliation(s)
- Jiahui Liao
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China
| | - Lanfang Huang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Meizi Qu
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Binghui Chen
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- *Correspondence: Binghui Chen
| | - Guojie Wang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Guojie Wang
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23
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Lee YJ, Kim YW, Ha J, Kim M, Guagliumi G, Granada JF, Lee SG, Lee JJ, Cho YK, Yoon HJ, Lee JH, Kim U, Jang JY, Oh SJ, Lee SJ, Hong SJ, Ahn CM, Kim BK, Chang HJ, Ko YG, Choi D, Hong MK, Jang Y, Lee JS, Kim JS. Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography—Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis. Front Cardiovasc Med 2022; 9:925414. [PMID: 35770218 PMCID: PMC9234158 DOI: 10.3389/fcvm.2022.925414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images. Methods Among patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR. Results Fusion-FFR was strongly correlated with FFR (r = 0.836, P < 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r = 0.682, P < 0.001; z statistic, 5.42, P < 0.001) and between FFR and OCT-FFR (r = 0.705, P < 0.001; z statistic, 4.38, P < 0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P = 0.024) and OCT-FFR (0.90 vs. 0.83, P = 0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P = 0.007; 61.4%, P < 0.001; 64.0%, P < 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012). Conclusion CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone. Clinical Trial Registration [www.ClinicalTrials.gov], identifier [NCT03298282].
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Affiliation(s)
- Yong-Joon Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young Woo Kim
- Department of Mechanical Engineering, Yonsei University, Seoul, South Korea
| | - Jinyong Ha
- Department of Electrical Engineering, Sejong University, Seoul, South Korea
| | - Minug Kim
- Department of Electrical Engineering, Sejong University, Seoul, South Korea
| | - Giulio Guagliumi
- Department of Cardiovascular, Ospedale Papa Giovanni XXIII, Bergamo, Italy
| | - Juan F. Granada
- Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY, United States
| | - Seul-Gee Lee
- Yonsei Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jung-Jae Lee
- Yonsei Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Yun-Kyeong Cho
- Department of Cardiology, Keimyung University Dongsan Hospital, Daegu, South Korea
| | - Hyuck Jun Yoon
- Department of Cardiology, Keimyung University Dongsan Hospital, Daegu, South Korea
| | - Jung Hee Lee
- Division of Cardiology, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, South Korea
| | - Ung Kim
- Division of Cardiology, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, South Korea
| | - Ji-Yong Jang
- National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Seung-Jin Oh
- National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Seung-Jun Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung-Jin Hong
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chul-Min Ahn
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Byeong-Keuk Kim
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young-Guk Ko
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Donghoon Choi
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Myeong-Ki Hong
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yangsoo Jang
- Division of Cardiology, CHA Bundang Medical Center, CHA University College of Medicine, Seongnam, South Korea
| | - Joon Sang Lee
- Department of Mechanical Engineering, Yonsei University, Seoul, South Korea
- *Correspondence: Joon Sang Lee,
| | - Jung-Sun Kim
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Jung-Sun Kim,
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Mickley H, Veien KT, Gerke O, Lambrechtsen J, Rohold A, Steffensen FH, Husic M, Akkan D, Busk M, Jessen LB, Jensen LO, Diederichsen A, Øvrehus KA. Diagnostic and Clinical Value of FFR CT in Stable Chest Pain Patients With Extensive Coronary Calcification: The FACC Study. JACC Cardiovasc Imaging 2022; 15:1046-1058. [PMID: 35680213 DOI: 10.1016/j.jcmg.2021.12.010] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 11/29/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND The influence of extensive coronary calcifications on the diagnostic and prognostic value of coronary computed tomography angiography-derived fractional flow reserve (FFRCT) has been scantily investigated. OBJECTIVES The purpose of this study was to investigate the diagnostic and short-term role of FFRCT in chest pain patients with Agatston score (AS) >399. METHODS This was a prospective multicenter study of 260 stable patients with suspected coronary artery disease (CAD) and AS >399. FFRCT was measured blinded by an independent core laboratory. All patients underwent invasive coronary angiography (ICA) and FFR if indicated. The agreement of FFRCT ≤0.80 with hemodynamically significant CAD on ICA/FFR (≥50% left main or ≥70% epicardial artery stenosis and/or FFR ≤0.80) was assessed. Patients undergoing FFR had colocation FFRCT measured, and the lowest per-patient FFRCT was registered in all patients. The association among per-patient FFRCT, coronary revascularization, and major clinical events (all-cause mortality, myocardial infarction, or unstable angina hospitalization) at 90-day follow-up was evaluated. RESULTS Median age and AS were 68.5 years (IQR: 63-74 years) and 895 (IQR: 587-1,513), respectively. FFRCT was ≤0.80 in 204 patients (78%). Colocation FFRCT (n = 112) showed diagnostic accuracy, sensitivity, and specificity to identify hemodynamically significant CAD of 71%, 87%, and 54%. The area under the receiver-operating characteristics curve (AUC) was 0.75. When using the lowest FFRCT (n = 260), per-patient accuracy, sensitivity, and specificity were 57%, 95%, and 32%, respectively. The AUC was 0.84. A total of 85 patients underwent revascularization, and FFRCT was ≤0.80 in 96% of these. During follow-up, major clinical events occurred in 3 patients (1.2%), all with FFRCT ≤0.80. CONCLUSIONS Most patients with AS >399 had FFRCT ≤0.80. Using ICA/FFR as the reference revealed a moderate diagnostic accuracy of colocation FFRCT. Compared with the lowest per-patient FFRCT, colocation FFRCT measurement improved diagnostic accuracy and specificity. The 90-day follow-up was favorable with few coronary revascularizations and no major clinical events occurring in patients with FFRCT >0.80. (Use of FFR-CT in Stable Intermediate Chest Pain Patients With Severe Coronary Calcium Score [FACC]; NCT03548753).
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Affiliation(s)
- Hans Mickley
- Department of Cardiology, Odense University Hospital, Odense, Denmark.
| | - Karsten T Veien
- Department of Cardiology, Odense University Hospital, Odense, Denmark
| | - Oke Gerke
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | | | - Allan Rohold
- Department of Cardiology, Esbjerg Hospital, Esbjerg, Denmark
| | | | - Mirza Husic
- Department of Cardiology, Svendborg Hospital, Svendborg, Denmark
| | - Dilek Akkan
- Department of Cardiology, Esbjerg Hospital, Esbjerg, Denmark
| | - Martin Busk
- Department of Cardiology, Vejle Hospital, Vejle, Denmark
| | - Louise B Jessen
- Department of Cardiology, Odense University Hospital, Odense, Denmark
| | - Lisette O Jensen
- Department of Cardiology, Odense University Hospital, Odense, Denmark
| | - Axel Diederichsen
- Department of Cardiology, Odense University Hospital, Odense, Denmark
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Li N, Li B, Liu J, Feng Y, Zhang L, Liu J, Liu Y. The quantitative relationship between coronary microcirculatory resistance and myocardial ischemia in patients with coronary artery disease. J Biomech 2022; 140:111166. [PMID: 35671542 DOI: 10.1016/j.jbiomech.2022.111166] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/17/2022] [Accepted: 05/23/2022] [Indexed: 10/18/2022]
Abstract
It was hypothesized that the microcirculatory resistance of resting state (Rm-res) might be a good predictor for ischemia. In this study, the quantitative relationship between Rm-res and myocardial ischemia in different stenosed degrees was explored and verified through retrospective analysis, and the diagnostic performance was evaluated. 136 patients were screened and divided into a training set (90 patients) and a validation set (46 patients). In the training set, Rm-res was calculated, and thresholds were determined by exploring the relationship between Rm-res and myocardial ischemia in different stenosed degrees. In the validation set, the diagnostic performance of the thresholds was verified. It was found that the 90 data mean difference (95%CI) of Rm-res between the ischemic group and the non-ischemic group was 63.03 (95 %CI: 25.72-100.34), p < 0.05. In the training set with stenosed degree 41-60%, 61-70%, 71-80%, and >81%, the average of Rm-res in the ischemic and non-ischemic groups were (80.79, 136.87), (96.41, 172.62), (128.99, 198.94) and (175.95, 310.79) mmHg/s/ml. The Rm-res thresholds were 87.18, 118.96, 142.35, and 177.39 mmHg/s/ml. In the validation set, the overall sensitivity, specificity, PPV, NPV, and accuracy were 73.3%, 77.4%, 61.1%, 85.7%, and 76.1%. In conclusion, Rm-res had a significant predictor on myocardial ischemia. As a smaller Rm-res represents greater myocardial mass perfusion, it is more likely that a stenosis will have a functional impact. Threshold analysis showed that Rm-res of different stenosed degrees was a quantitative predictor of myocardial ischemia, which could assist physicians with clinical treatment strategies.
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Affiliation(s)
- Na Li
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Bao Li
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jincheng Liu
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Yili Feng
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Liyuan Zhang
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jian Liu
- Peking University People's Hospital, Beijing, China
| | - Youjun Liu
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China.
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26
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Bray JJH, Hanif MA, Alradhawi M, Ibbetson J, Dosanjh SS, Smith SL, Ahmad M, Pimenta D. Machine learning applications in cardiac computed tomography: a composite systematic review. EUROPEAN HEART JOURNAL OPEN 2022; 2:oeac018. [PMID: 35919128 PMCID: PMC9242067 DOI: 10.1093/ehjopen/oeac018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/10/2022] [Indexed: 12/02/2022]
Abstract
Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.
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Affiliation(s)
- Jonathan James Hyett Bray
- Institute of Life Sciences 2, Swansea University Medical, School , Swansea, UK
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | - Moghees Ahmad Hanif
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | | | - Jacob Ibbetson
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | | | - Sabrina Lucy Smith
- Barts and the London School of Medicine and Dentistry , London E1 2AD, UK
| | - Mahmood Ahmad
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
- University College London Medical School , London WC1E 6DE, UK
| | - Dominic Pimenta
- Richmond Research Institute, St George’s Hospital, University of London , Cranmer Terrace, Tooting, London SW17 0RE, UK
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27
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Rajiah P, Cummings KW, Williamson E, Young PM. CT Fractional Flow Reserve: A Practical Guide to Application, Interpretation, and Problem Solving. Radiographics 2022; 42:340-358. [PMID: 35119968 DOI: 10.1148/rg.210097] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
CT fractional flow reserve (FFRCT) is a physiologic simulation technique that models coronary flow from routine coronary CT angiography (CTA). To evaluate lesion-specific ischemia, FFRCT is measured 2 cm distal to a stenotic lesion. FFRCT greater than 0.8 is normal, 0.76-0.8 is borderline, and 0.75 or less is abnormal. FFRCT should always be interpreted in correlation with clinical and anatomic coronary CTA findings. FFRCT increases the specificity of coronary CTA in the evaluation of coronary artery disease, decreases the prevalence of nonobstructive disease in invasive coronary angiography (ICA), and helps with revascularization decisions and planning. Patients with intermediate-risk coronary anatomy at CTA and abnormal FFRCT can undergo ICA and revascularization, whereas those with normal FFRCT can be safely deferred from ICA. In borderline FFRCT values, management is decided in the context of the clinical scenario, but many cases could be safely managed with medical treatment. There are some limitations and pitfalls of FFRCT. Abnormal FFRCT values can be seen in mild stenosis, and normal FFRCTvalues can be seen in severe stenosis. Gradually decreasing or abnormal low FFRCT values at the distal vessel without a proximal focal lesion could be due to diffuse atherosclerosis. Coronary stents, bypass grafts, coronary anomalies, coronary dissection, transcatheter aortic valve replacement, unstable angina, and acute or recent myocardial infarction are situations in which FFRCT has not been validated and should not be used at this time. The authors provide a practical guide to the applications and interpretation of FFRCT, focusing on common pitfalls and challenges. Online supplemental material is available for this article. ©RSNA, 2022.
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Affiliation(s)
- Prabhakar Rajiah
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905 (P.R., E.W., P.M.Y.); and Department of Radiology, Mayo Clinic, Phoenix, Ariz (K.W.C.)
| | - Kristopher W Cummings
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905 (P.R., E.W., P.M.Y.); and Department of Radiology, Mayo Clinic, Phoenix, Ariz (K.W.C.)
| | - Eric Williamson
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905 (P.R., E.W., P.M.Y.); and Department of Radiology, Mayo Clinic, Phoenix, Ariz (K.W.C.)
| | - Phillip M Young
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905 (P.R., E.W., P.M.Y.); and Department of Radiology, Mayo Clinic, Phoenix, Ariz (K.W.C.)
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28
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Gohmann RF, Seitz P, Pawelka K, Majunke N, Schug A, Heiser L, Renatus K, Desch S, Lauten P, Holzhey D, Noack T, Wilde J, Kiefer P, Krieghoff C, Lücke C, Ebel S, Gottschling S, Borger MA, Thiele H, Panknin C, Abdel-Wahab M, Horn M, Gutberlet M. Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease. J Clin Med 2022; 11:1331. [PMID: 35268422 PMCID: PMC8910873 DOI: 10.3390/jcm11051331] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 02/06/2023] Open
Abstract
Background: Coronary artery disease (CAD) is a frequent comorbidity in patients undergoing transcatheter aortic valve implantation (TAVI). If significant CAD can be excluded on coronary CT-angiography (cCTA), invasive coronary angiography (ICA) may be avoided. However, a high plaque burden may make the exclusion of CAD challenging, particularly for less experienced readers. The objective was to analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to correctly categorize cCTA studies without obstructive CAD acquired during pre-TAVI evaluation and to correlate recategorization to image quality and coronary artery calcium score (CAC). Methods: In total, 116 patients without significant stenosis (≥50% diameter) on cCTA as part of pre-TAVI CT were included. Patients were examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the torso. Patients were re-evaluated with ML-based CT-FFR (threshold = 0.80). The standard of reference was ICA. Image quality was assessed quantitatively and qualitatively. Results: ML-based CT-FFR was successfully performed in 94.0% (109/116) of patients, including 436 vessels. With CT-FFR, 76/109 patients and 126/436 vessels were falsely categorized as having significant CAD. With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. Reclassification occurred predominantly in distal segments. Virtually no correlation was found between image quality or CAC. Conclusions: Unselectively applied, CT-FFR may vastly increase the number of false positive ratings of CAD compared to morphological scoring. Recategorization was virtually independently from image quality or CAC and occurred predominantly in distal segments. It is unclear whether or not the reduced CT-FFR represent true pressure ratios and potentially signifies pathophysiology in patients with severe aortic stenosis.
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Affiliation(s)
- Robin Fabian Gohmann
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Patrick Seitz
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
| | - Konrad Pawelka
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Nicolas Majunke
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (N.M.); (S.D.); (P.L.); (J.W.); (H.T.); (M.A.-W.)
| | - Adrian Schug
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Linda Heiser
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
| | - Katharina Renatus
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Steffen Desch
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (N.M.); (S.D.); (P.L.); (J.W.); (H.T.); (M.A.-W.)
| | - Philipp Lauten
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (N.M.); (S.D.); (P.L.); (J.W.); (H.T.); (M.A.-W.)
| | - David Holzhey
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (D.H.); (T.N.); (P.K.); (M.A.B.)
| | - Thilo Noack
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (D.H.); (T.N.); (P.K.); (M.A.B.)
| | - Johannes Wilde
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (N.M.); (S.D.); (P.L.); (J.W.); (H.T.); (M.A.-W.)
| | - Philipp Kiefer
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (D.H.); (T.N.); (P.K.); (M.A.B.)
| | - Christian Krieghoff
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
| | - Christian Lücke
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
| | - Sebastian Ebel
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
| | - Sebastian Gottschling
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
| | - Michael A. Borger
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (D.H.); (T.N.); (P.K.); (M.A.B.)
- Leipzig Heart Institute, Russenstr. 69a, 04289 Leipzig, Germany
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (N.M.); (S.D.); (P.L.); (J.W.); (H.T.); (M.A.-W.)
- Leipzig Heart Institute, Russenstr. 69a, 04289 Leipzig, Germany
| | | | - Mohamed Abdel-Wahab
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (N.M.); (S.D.); (P.L.); (J.W.); (H.T.); (M.A.-W.)
| | - Matthias Horn
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany;
| | - Matthias Gutberlet
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; (P.S.); (K.P.); (A.S.); (L.H.); (K.R.); (C.K.); (C.L.); (S.E.); (S.G.); (M.G.)
- Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany
- Leipzig Heart Institute, Russenstr. 69a, 04289 Leipzig, Germany
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Dai X, Lu Z, Yu Y, Yu L, Xu H, Zhang J. The use of lesion-specific calcium morphology to guide the appropriate use of dynamic CT myocardial perfusion imaging and CT fractional flow reserve. Quant Imaging Med Surg 2022; 12:1257-1269. [PMID: 35111621 DOI: 10.21037/qims-21-491] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/18/2021] [Indexed: 12/28/2022]
Abstract
Background We aimed to optimize the diagnostic strategy for dynamic computed tomography myocardial perfusion imaging (CT-MPI) and CT fractional flow reserve (CT-FFR) in the evaluation of coronary artery disease (CAD). Methods Patients who had undergone coronary CT angiography (CCTA) + dynamic CT-MPI and invasive coronary angiography (ICA)/FFR within a 4-week period were retrospectively included. Lesion-specific characteristics were recorded, and multivariate logistic regression was performed to determine the predictors of mismatched CT findings with ICA results. An optimized diagnostic strategy was proposed based on the diagnostic performance of dynamic CT-MPI and CT-FFR compared with ICA/FFR. A net reclassification index (NRI) was calculated to determine the incremental discriminatory power of optimized CT-FFR + dynamic CT-MPI strategy compared to CT-FFR alone. Results The study included 180 patients with 229 diseased vessels. For CT-FFR, a calcified lesion with a calcium arc >180° was the only independent predictor for misdiagnosis of ischemic coronary stenosis (odds ratio =2.367; P=0.002). For noncalcified lesions and calcified lesions with a calcium arc ≤180°, the sensitivity and negative predictive value (NPV) of CT-FFR were similar to those of CT-MPI (all P values >0.05), whereas the specificity and positive predictive value (PPV) of CT-FFR were significantly lower (all P values <0.05). For calcified lesions with a calcium arc >180°, the specificity, NPV, and PPV of CT-FFR were inferior to those of CT-MPI (21.2% vs. 100%, 58.3% vs. 86.8%, and 62.9% vs. 100%, respectively; all P values <0.05). As guided by lesion-specific calcium morphology, an optimized CT-FFR + dynamic CT-MPI strategy (NRI =0.2; P=0.004) would have resulted in a 27.0% and 33.9% reduction of radiation dose and contrast medium consumption, respectively, and 25.3% of patients would have avoided unnecessary invasive tests. Conclusions The diagnostic performance of CT-FFR was significantly inferior in lesions with a calcium arc >180°. Lesion-specific calcium morphology is the preferred parameter to guide the appropriate use of CT-based functional assessment.
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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
| | - Zhigang Lu
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yarong Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lihua Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Xu
- Department of Cardiology, 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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Current and Future Applications of Artificial Intelligence in Coronary Artery Disease. Healthcare (Basel) 2022; 10:healthcare10020232. [PMID: 35206847 PMCID: PMC8872080 DOI: 10.3390/healthcare10020232] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.
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Li N, Li B, Feng Y, Ma J, Zhang L, Liu J, Liu Y. Impact of coronary bifurcated vessels flow-diameter scaling laws on fractional flow reserve based on computed tomography images (FFRCT). MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3127-3146. [PMID: 35240824 DOI: 10.3934/mbe.2022145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To explore the influence of the blood flow-diameter scaling laws of $ \mathrm{Q}\mathrm{\alpha }{\mathrm{D}}^{3} $, $ \mathrm{Q}\mathrm{\alpha }{\mathrm{D}}^{2.7} $ and $ \text{Q}\alpha \text{D}{}^{7}\!\!\diagup\!\!{}_{3}\; $ on the numerical simulation of fraction flow reserve based on CTA images and to find the optimal exponents. METHODS 1) 26 patients with coronary artery disease were screened according to the inclusion criteria; 2) Microcirculation resistance (Rm) was calculated under the 3, 2.7 and 7/3 power of the flow-diameter scaling law, which were recorded as 3Rm, 2.7Rm and 7/3Rm, respectively; 3) 3Rm, 2.7Rm and 7/3Rm were used as exit boundary conditions to simulate FFRCT, quoted as 3FFRCT, 2.7FFRCT and 7/3FFRCT, respectively; 4) The correlation and diagnostic performance between three kinds of FFRCT and FFR were analyzed. RESULTS The p-values of comparing 3Rm, 2.7Rm and 7/3Rm with FFR were 0.004, 0.005 and 0.010, respectively; the r value between 7/3FFRCT and FFR (0.96) was better than that of 3FFRCT (0.95) and 2.7FFRCT (0.95); the 95% LoA between 7/3FFRCT and FFR (-0.08~0.11) was smaller than that of 3FFRCT (-0.10~0.12) and 2.7FFRCT (-0.09~0.11); the AUC and accuracy of 7/3FFRCT [0.962 (0.805-0.999), 96.15%] were the same as those of 2.7FFRCT [0.962 (0.805-0.999), 96.15%] and better than those of 3FFRCT [0.944 (0.777-0.996), 92.3%]. The prediction threshold of 7/3FFRCT (0.791) was closer to 0.8 than that of 3FFRCT (0.816) and 2.7FFRCT (0.787). CONCLUSION The blood flow-diameter scaling law affects the FFRCT simulation by influencing the exit boundary condition Rm of the calculation. With $ Q\alpha D{}^{7}\!\!\diagup\!\!{}_{3}\; $, FFRCT had the highest diagnostic performance. The blood flow-diameter scaling law provides theoretical support for the blood flow distribution in the bifurcated vessel and improves the FFRCT model.
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Affiliation(s)
- Na Li
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Bao Li
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Yili Feng
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Junling Ma
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Liyuan Zhang
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jian Liu
- Peking University People's Hospital, Beijing, China
| | - Youjun Liu
- College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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Zhao N, Gao Y, Xu B, Yang W, Song L, Jiang T, Xu L, Hu H, Li L, Chen W, Li D, Zhang F, Fan L, Lu B. Effect of Coronary Calcification Severity on Measurements and Diagnostic Performance of CT-FFR With Computational Fluid Dynamics: Results From CT-FFR CHINA Trial. Front Cardiovasc Med 2022; 8:810625. [PMID: 35047581 PMCID: PMC8761984 DOI: 10.3389/fcvm.2021.810625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 11/30/2021] [Indexed: 11/28/2022] Open
Abstract
Aims: To explore the effect of coronary calcification severity on the measurements and diagnostic performance of computed tomography-derived fractional flow reserve (FFR; CT-FFR). Methods: This study included 305 patients (348 target vessels) with evaluable coronary calcification (CAC) scores from CT-FFR CHINA clinical trial. The enrolled patients all received coronary CT angiography (CCTA), CT-FFR, and invasive FFR examinations within 7 days. On both per-patient and per-vessel levels, the measured values, accuracy, and diagnostic performance of CT-FFR in identifying hemodynamically significant lesions were analyzed in all CAC score groups (CAC = 0, > 0 to <100, ≥ 100 to <400, and ≥ 400), with FFR as reference standard. Results: In total, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under receiver operating characteristics curve (AUC) of CT-FFR were 85.8, 88.7, 86.9, 87.8, 87.1%, 0.90 on a per-patient level and 88.3, 89.3, 89.5, 88.2, 88.9%, 0.88 on a per-vessel level, respectively. Absolute difference of CT-FFR and FFR values tended to elevate with increased CAC scores (CAC = 0: 0.09 ± 0.10; CAC > 0 to <100: 0.06 ± 0.06; CAC ≥ 100 to <400: 0.09 ± 0.10; CAC ≥ 400: 0.11 ± 0.13; p = 0.246). However, no statistically significant difference was found in patient-based and vessel-based diagnostic performance of CT-FFR among all CAC score groups. Conclusion: This prospective multicenter trial supported CT-FFR as a viable tool in assessing coronary calcified lesions. Although large deviation of CT-FFR has a tendency to correlate with severe calcification, coronary calcification has no significant influence on CT-FFR diagnostic performance using the widely-recognized cut-off value of 0.8.
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Affiliation(s)
- Na Zhao
- Department of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Gao
- Department of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Yang Gao
| | - Bo Xu
- Catheterization Laboratories, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weixian Yang
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lei Song
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Jiang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Li Xu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lin Li
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenqiang Chen
- Department of Cardiology, Qilu Hospital of Shandong University, Jinan, China
| | - Dumin Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Feng Zhang
- Department of Cardiology, Teda International Cardiovascular Hospital, Tianjin, China
| | - Lijuan Fan
- Department of Radiology, Teda International Cardiovascular Hospital, Tianjin, China
| | - Bin Lu
- Department of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Bin Lu
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Kim MY, Yang DH, Choo KS, Lee W. Beyond Coronary CT Angiography: CT Fractional Flow Reserve and Perfusion. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:3-27. [PMID: 36237355 PMCID: PMC9238199 DOI: 10.3348/jksr.2021.0177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/15/2021] [Accepted: 12/27/2021] [Indexed: 11/30/2022]
Abstract
심장 전산화단층촬영은 비약적인 기술발전과 다양한 연구 결과를 바탕으로 심혈관위험 계층화와 치료 결정을 위한 관상동맥 질환의 진단과 예후 평가성능이 입증되었다. 전산화단층촬영 관상동맥조영술은 폐쇄성 관상동맥 질환에 대한 음성 예측도가 높아서 침습적 혈관조영술의 빈도를 줄일 수 있는 관상동맥 질환 관련 검사의 관문으로 부상했지만, 진단특이도가 상대적으로 낮다. 하지만 심장 전산화단층촬영을 이용한 분획혈류예비력과 심근관류를 분석하여 관상동맥 질환의 혈역학적 유의성을 확인하는 기능적 평가를 통해 그 한계를 극복할 수 있다. 최근에는 이를 보다 객관적이고 재현 가능하도록 인공지능을 접목하는 연구들이 활발히 진행되고 있다. 본 종설에서는 심장 전산화단층촬영의 기능적 영상화 기법들에 대해 알아보고자 한다.
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Affiliation(s)
- Moon Young Kim
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ki Seok Choo
- Department of Radiology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea
| | - Whal Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
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Infante T, Cavaliere C, Punzo B, Grimaldi V, Salvatore M, Napoli C. Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review. Circ Cardiovasc Imaging 2021; 14:1133-1146. [PMID: 34915726 DOI: 10.1161/circimaging.121.013025] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.
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Affiliation(s)
- Teresa Infante
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.)
| | | | - Bruna Punzo
- IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
| | | | | | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.).,IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
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Shaw LJ, Chandrashekhar Y. What Is of Recent Interest in Cardiac Imaging?: Insights From the JACC Family of Journals. J Am Coll Cardiol 2021; 78:2387-2391. [PMID: 34857099 PMCID: PMC8629342 DOI: 10.1016/j.jacc.2021.10.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Gohmann RF, Pawelka K, Seitz P, Majunke N, Heiser L, Renatus K, Desch S, Lauten P, Holzhey D, Noack T, Wilde J, Kiefer P, Krieghoff C, Lücke C, Gottschling S, Ebel S, Borger MA, Thiele H, Panknin C, Horn M, Abdel-Wahab M, Gutberlet M. Combined Coronary CT-Angiography and TAVR Planning for Ruling Out Significant Coronary Artery Disease: Added Value of Machine-Learning-Based CT-FFR. JACC Cardiovasc Imaging 2021; 15:476-486. [PMID: 34801449 DOI: 10.1016/j.jcmg.2021.09.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 01/09/2023]
Abstract
OBJECTIVES To analyze the ability of machine-learning (ML)-based computed tomography (CT)-derived fractional flow reserve (CT-FFR) to further improve the diagnostic performance of coronary CT angiography (cCTA) for ruling out significant coronary artery disease (CAD) during pre-transcatheter aortic valve replacement (TAVR) evaluation in patients with a high pre-test probability for CAD. BACKGROUND CAD is a frequent comorbidity in patients undergoing TAVR. Current guidelines recommend its assessment before TAVR. If significant CAD can be excluded on cCTA, invasive coronary angiography (ICA) may be avoided. Although cCTA is a very sensitive test, it is limited by relatively low specificity and positive predictive value, particularly in high-risk patients. METHODS Overall, 460 patients (79.6 ± 7.4 years) undergoing pre-TAVR CT were included and examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the vascular access route. Images were evaluated for significant CAD. Patients routinely underwent ICA (388/460), which was omitted at the discretion of the local Heart Team if CAD could be effectively ruled out on cCTA (72/460). CT examinations in which CAD could not be ruled out (CAD+) (n = 272) underwent additional ML-based CT-FFR. RESULTS ML-based CT-FFR was successfully performed in 79.4% (216/272) of all CAD+ patients and correctly reclassified 17 patients as CAD negative. CT-FFR was not feasible in 20.6% because of reduced image quality (37/56) or anatomic variants (19/56). Sensitivity, specificity, positive predictive value, and negative predictive value were 94.9%, 52.0%, 52.2%, and 94.9%, respectively. The additional evaluation with ML-based CT-FFR increased accuracy by Δ+3.4% (CAD+: Δ+6.0%) and raised the total number of examinations negative for CAD to 43.9% (202/460). CONCLUSIONS ML-based CT-FFR may further improve the diagnostic performance of cCTA by correctly reclassifying a considerable proportion of patients with morphological signs of obstructive CAD on cCTA during pre-TAVR evaluation. Thereby, CT-FFR has the potential to further reduce the need for ICA in this challenging elderly group of patients before TAVR.
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Affiliation(s)
- Robin F Gohmann
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany; Medical Faculty, University of Leipzig, Leipzig, Germany.
| | - Konrad Pawelka
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany; Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Patrick Seitz
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
| | - Nicolas Majunke
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Linda Heiser
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
| | - Katharina Renatus
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany; Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Steffen Desch
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Philipp Lauten
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - David Holzhey
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Thilo Noack
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Johannes Wilde
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Philipp Kiefer
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Christian Krieghoff
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
| | - Christian Lücke
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
| | - Sebastian Gottschling
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany
| | - Sebastian Ebel
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany; Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Michael A Borger
- Department of Cardiac Surgery, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute, Leipzig, Germany
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute, Leipzig, Germany
| | | | - Matthias Horn
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Mohamed Abdel-Wahab
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Matthias Gutberlet
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany; Medical Faculty, University of Leipzig, Leipzig, Germany; Leipzig Heart Institute, Leipzig, Germany
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Lin A, Kolossváry M, Motwani M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence in cardiovascular CT: Current status and future implications. J Cardiovasc Comput Tomogr 2021; 15:462-469. [PMID: 33812855 PMCID: PMC8455701 DOI: 10.1016/j.jcct.2021.03.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/29/2021] [Accepted: 03/15/2021] [Indexed: 12/23/2022]
Abstract
Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Márton Kolossváry
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Manish Motwani
- Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | | | - Piotr J Slomka
- Artificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Seetharam K, Bhat P, Orris M, Prabhu H, Shah J, Asti D, Chawla P, Mir T. Artificial intelligence and machine learning in cardiovascular computed tomography. World J Cardiol 2021; 13:546-555. [PMID: 34754399 PMCID: PMC8554359 DOI: 10.4330/wjc.v13.i10.546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 07/10/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virgina University, Morgan Town, NY 26501, United States.
| | - Premila Bhat
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Maxine Orris
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Hejmadi Prabhu
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Jilan Shah
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Deepak Asti
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Preety Chawla
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Tanveer Mir
- Department of Internal Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
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Maragna R, Giacari CM, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rossi A, Rabbat M, Pontone G. Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management. Front Cardiovasc Med 2021; 8:736223. [PMID: 34631834 PMCID: PMC8493089 DOI: 10.3389/fcvm.2021.736223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/25/2021] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.
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Affiliation(s)
- Riccardo Maragna
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Carlo Maria Giacari
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Marco Guglielmo
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Baggiano
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy.,Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Laura Fusini
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Igoren Guaricci
- Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital Policlinico of Bari, Bari, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,Center for Molecular Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Mark Rabbat
- Department of Medicine and Radiology, Division of Cardiology, Loyola University of Chicago, Chicago, IL, United States.,Department of Medicine, Division of Cardiology, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Gianluca Pontone
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
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40
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Choe YH. Outcomes of Coronary CT Fractional Flow Reserve in Patients Referred for Transcatheter Aortic Valve Replacement. Radiology 2021; 302:59-60. [PMID: 34609201 DOI: 10.1148/radiol.2021211996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Yeon Hyeon Choe
- From the Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Ilwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea
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Aquino GJ, Abadia AF, Schoepf UJ, Emrich T, Yacoub B, Kabakus I, Violette A, Wiley C, Moreno A, Sahbaee P, Schwemmer C, Bayer RR, Varga-Szemes A, Steinberg D, Amoroso N, Kocher M, Waltz J, Ward TJ, Burt JR. Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes. Radiology 2021; 302:50-58. [PMID: 34609200 DOI: 10.1148/radiol.2021210160] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background The role of CT angiography-derived fractional flow reserve (CT-FFR) in pre-transcatheter aortic valve replacement (TAVR) assessment is uncertain. Purpose To evaluate the predictive value of on-site machine learning-based CT-FFR for adverse clinical outcomes in candidates for TAVR. Materials and Methods This observational retrospective study included patients with severe aortic stenosis referred to TAVR after coronary CT angiography (CCTA) between September 2014 and December 2019. Clinical end points comprised major adverse cardiac events (MACE) (nonfatal myocardial infarction, unstable angina, cardiac death, or heart failure admission) and all-cause mortality. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm. The ability of CT-FFR (abnormal if ≤0.75) to predict outcomes and improve the predictive value of the current noninvasive work-up was assessed. Survival analysis was performed, and the C-index was used to assess the performance of each predictive model. To compare nested models, the likelihood ratio χ2 test was performed. Results A total of 196 patients (mean age ± standard deviation, 75 years ± 11; 110 women [56%]) were included; the median time of follow-up was 18 months. MACE occurred in 16% (31 of 196 patients) and all-cause mortality in 19% (38 of 196 patients). Univariable analysis revealed CT-FFR was predictive of MACE (hazard ratio [HR], 4.1; 95% CI: 1.6, 10.8; P = .01) but not all-cause mortality (HR, 1.2; 95% CI: 0.6, 2.2; P = .63). CT-FFR was independently associated with MACE (HR, 4.0; 95% CI: 1.5, 10.5; P = .01) when adjusting for potential confounders. Adding CT-FFR as a predictor to models that include CCTA and clinical data improved their predictive value for MACE (P = .002) but not all-cause mortality (P = .67), and it showed good discriminative ability for MACE (C-index, 0.71). Conclusion CT angiography-derived fractional flow reserve was associated with major adverse cardiac events in candidates for transcatheter aortic valve replacement and improved the predictive value of coronary CT angiography assessment. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Choe in this issue.
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Affiliation(s)
- Gilberto J Aquino
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Andres F Abadia
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - U Joseph Schoepf
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Tilman Emrich
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Basel Yacoub
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Ismail Kabakus
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Alexis Violette
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Courtney Wiley
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Andreina Moreno
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Pooyan Sahbaee
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Chris Schwemmer
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Richard R Bayer
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Akos Varga-Szemes
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Daniel Steinberg
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Nicholas Amoroso
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Madison Kocher
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Jeffrey Waltz
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Thomas J Ward
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
| | - Jeremy R Burt
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (G.J.A., A.F.A., U.J.S., T.E., B.Y., I.K., A.V., C.W., A.M., R.R.B., A.V.S., M.K., J.W., J.R.B.), and Division of Cardiology, Department of Medicine (R.R.B., D.S., N.A.), Medical University of South Carolina, 25 Courtenay Dr, MSC 226, Room 2301, Charleston, SC 29425-2503; Siemens Medical Solutions, Malvern, Pa (P.S.); Siemens Healthineers, Forchheim, Germany (C.S.); and Department of Radiology, Florida Hospital, Orlando, Fla (T.J.W.)
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Nørgaard BL, Mortensen MB, Parner E, Leipsic J, Steffensen FH, Grove EL, Mathiassen ON, Sand NP, Pedersen K, Riedl KA, Engholm M, Bøtker HE, Jensen JM. Clinical outcomes following real-world computed tomography angiography-derived fractional flow reserve testing in chronic coronary syndrome patients with calcification. Eur Heart J Cardiovasc Imaging 2021; 22:1182-1189. [PMID: 32793947 DOI: 10.1093/ehjci/jeaa173] [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: 01/08/2020] [Revised: 03/23/2020] [Accepted: 05/29/2020] [Indexed: 11/13/2022] Open
Abstract
AIMS This study sought to investigate outcomes following a normal CT-derived fractional flow reserve (FFRCT) result in patients with moderate stenosis and coronary artery calcification, and to describe the relationship between the extent of calcification, stenosis, and FFRCT. METHODS AND RESULTS Data from 975 consecutive patients suspected of chronic coronary syndrome with stenosis (30-70%) determined by computed CT angiography and FFRCT to guide downstream management decisions were reviewed. Median (range) follow-up time was 2.2 (0.5-4.2) years. Coronary artery calcium (CAC) scores were ≥400 in 25%, stenosis ≥50% in 83%, and FFRCT >0.80 in 51% of the patients. There was a lower incidence of the composite endpoint (death, myocardial infarction, hospitalization for unstable angina, and unplanned coronary revascularization) at 4.2 years in patients with any CAC and FFRCT > 0.80 vs. FFRCT ≤ 0.80 (3.9% and 8.7%, P = 0.04), however, in patients with CAC scores ≥400 the risk difference between groups did not reach statistical significance, 4.2% vs. 9.7% (P = 0.24). A negative relationship between CAC scores and FFRCT irrespective of stenosis severity was demonstrated. CONCLUSION FFRCT shows promise in identifying patients with stenosis and calcification who can be managed without further downstream testing. Moreover, an inverse relationship between CAC levels and FFRCT was demonstrated. Studies are needed to further assess the clinical utility of FFRCT in patients with extensive coronary calcification.
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Affiliation(s)
- Bjarne L Nørgaard
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
| | - Martin B Mortensen
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
| | - Erik Parner
- Department of Public Health, Section for Biostatistics, Aarhus University, Bartholins Alle 2, 8000 Aarhus C, Denmark
| | - Jonathon Leipsic
- Department of Radiology, St. Pauls Hospital, University of British Columbia, 1081 Burrard St, Vancouver, BC V6Z 1Y6, Canada
| | | | - Erik Lerkevang Grove
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
| | - Ole N Mathiassen
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
| | - Niels Peter Sand
- Department of Cardiology, Hospital of Southwest Jutland, Finsensgade 35, 6700 Esbjerg, Denmark
| | - Kamilla Pedersen
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
| | - Katharina A Riedl
- Department of Cardiology, University Heart and vascular Center, Martinistrase 52, 20246 Hamburg, Germany
| | - Morten Engholm
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
| | - Hans Erik Bøtker
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
| | - Jesper M Jensen
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensen Blv 99, 8200 Aarhus N, Denmark
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Yun CH, Hung CL, Wen MS, Wan YL, So A. CT Assessment of Myocardial Perfusion and Fractional Flow Reserve in Coronary Artery Disease: A Review of Current Clinical Evidence and Recent Developments. Korean J Radiol 2021; 22:1749-1763. [PMID: 34431244 PMCID: PMC8546143 DOI: 10.3348/kjr.2020.1277] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 05/11/2021] [Accepted: 05/15/2021] [Indexed: 11/25/2022] Open
Abstract
Coronary computed tomography angiography (CCTA) is routinely used for anatomical assessment of coronary artery disease (CAD). However, invasive measurement of fractional flow reserve (FFR) is the current gold standard for the diagnosis of hemodynamically significant CAD. CT-derived FFRCT and CT perfusion are two emerging techniques that can provide a functional assessment of CAD for risk stratification and clinical decision making. Several clinical studies have shown that the diagnostic performance of concomitant CCTA and functional CT assessment for detecting hemodynamically significant CAD is at least non-inferior to that of other routinely used imaging modalities. This article aims to review the current clinical evidence and recent developments in functional CT techniques.
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Affiliation(s)
- Chun-Ho Yun
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
| | - Chung-Lieh Hung
- Division of Cardiology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan.,Institute of Biomedical Sciences, Mackay Medical College, New Taipei, Taiwan
| | - Ming-Shien Wen
- Department of Cardiology, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yung-Liang Wan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Aaron So
- Department of Medical Biophysics, University of Western Ontario, Imaging Program, Lawson Health Research Institute, London, Canada
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Fukuoka R, Kawasaki T, Umeji K, Okonogi T, Koga N. The diagnostic performance of on-site workstation-based computed tomography-derived fractional flow reserve. Comparison with myocardium perfusion imaging. Heart Vessels 2021; 37:22-30. [PMID: 34263357 DOI: 10.1007/s00380-021-01897-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/25/2021] [Indexed: 11/28/2022]
Abstract
To compare the diagnostic performance of on-site workstation-based computed tomography-derived fractional flow reserve (CT-FFR)Few data of CT-FFR were reported regarding the diagnostic performance for detecting hemodynamically significant coronary artery disease (CAD). This retrospective single-center analysis included 132 vessels in 77 patients who underwent CT angiography, myocardial perfusion imaging (MPI), and invasive FFR. The correlation coefficient between CT-FFR and invasive FFR and optimal cut-off value for CT-FFR to identify invasive FFR ≤ 0.8 were evaluated. The diagnostic accuracies of CT- FFR, and MPI were evaluated using an area under the receiver-operating characteristic curve (AUC) with invasive FFR as a reference standard. Diagnostic performance of CT-FFR was also evaluated concerning lesion characteristics, including intermediate lesions, left main lesions, tandem lesions, and/or diffuse lesions, and coronary calcium (Agatston score over 400). The Receiver Operating Characteristic curve analysis showed that the optimal cut-off value of CT-FFR for detecting invasive FFR ≤ 0.80 was 0.80 [AUC = 0.83, 95%CI: 0.76-0.90). Diagnostic sensitivity, specificity, positive and negative predictive value, and accuracy of CT-FFR when compared with those of MPI regarding per-patient analysis were 93% vs. 63%, 48% vs. 61%, 81% vs. 79%, 73% vs. 41%, and 79% vs. 62%, respectively, and for per-vessel analysis were 89% vs. 24%, 66% vs. 82%, 75% vs. 61%, 83% vs. 48%, and 78% vs. 51%, respectively. The AUC of the CT-FFR was significantly higher than MPI (0.83 vs. 0.57, p < 0.0001) regarding the per-vessel analysis. No differences in the diagnostic performance of CT-FFR were noted in the presence of intermediate lesions, left main lesions, tandem lesions, and/or diffuse lesions, and severe coronary calcium. On-site CT-FFR delivered a higher diagnostic performance than MPI for detecting CAD with invasive FFR ≤ 0.8, indicating the potential of CT-FFR as the gatekeeper of invasive coronary angiogram as well as percutaneous coronary intervention.
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Affiliation(s)
- Ryota Fukuoka
- Department of Cardiology Cardiovascular and Heart Rhythm Center, Shin-Koga Hospital, 120, Kurume, Tenjin-machi, 830-8577, Japan.
| | - Tomohiro Kawasaki
- Department of Cardiology Cardiovascular and Heart Rhythm Center, Shin-Koga Hospital, 120, Kurume, Tenjin-machi, 830-8577, Japan
| | - Kyoko Umeji
- Department of Cardiology Cardiovascular and Heart Rhythm Center, Shin-Koga Hospital, 120, Kurume, Tenjin-machi, 830-8577, Japan
| | - Taichi Okonogi
- Department of Emergency, Shin-Koga Hospital, 120, Kurume, Tenjin-machi, 830-8577, Japan
| | - Nobuhiko Koga
- Department of Cardiology Cardiovascular and Heart Rhythm Center, Shin-Koga Hospital, 120, Kurume, Tenjin-machi, 830-8577, Japan
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Real-world clinical and cost analysis of CT coronary angiography and CT coronary angiography-derived fractional flow reserve (FFR CT)-guided care in the National Health Service. Clin Radiol 2021; 76:862.e19-862.e28. [PMID: 34261595 DOI: 10.1016/j.crad.2021.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 06/15/2021] [Indexed: 12/28/2022]
Abstract
AIM To quantify the real-world clinical and cost impact of computed tomography (CT) coronary angiography (CTCA)-derived fractional flow reserve (FFRCT) in the National Health Service (NHS). MATERIALS AND METHODS Consecutive clinical CTCA examinations from September to December 2018 with ≥1 stenosis of ≥25% underwent FFRCT analysis. The Heart Team reviewed clinical data and CTCA findings, blinded to FFRCT values, and documented hypothetical consensus management. FFRCT results were then unblinded and hypothetical consensus management re-recorded. Diagnostic waiting times for management pathways were estimated. A per-patient cost analysis for diagnostic certainty regarding coronary artery disease (CAD) management was performed using 2014-2020 NHS tariffs for pre- and post-FFRCT pathways. RESULTS Two hundred and fifty-one CTCAs were performed during the study period. Fifty-seven percent (145/251) had no CAD or stenosis <25%. One study was non-diagnostic. Of the remaining 42% (105/251), two were ineligible for FFRCT and there was a 5% (5/103) failure rate. FFRCT led to a change in hypothetical management in 65% (64/98; p<0.001) patients with a functional imaging test cancelled in 17% (17/98) and a diagnostic angiogram cancelled in 47% (46/98). FFRCT-guided management had a reduced mean time to definitive investigation compared with CTCA alone (28 ± 4 versus 44 ± 4 days; p=0.004). Using the proposed 2020/21 tariff, CTCA + FFRCT for stenosis ≥50% resulted in a diagnostic pathway £44.97 more expensive per patient than usual care without FFRCT. CONCLUSIONS In the real-world NHS setting, FFRCT-guided management has the potential to rationalise patient management, accelerate diagnostic pathways, and depending on the stenosis severity modelled, may be cost-effective.
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Henriksson L, Woisetschläger M, Alfredsson J, Janzon M, Ebbers T, Engvall J, Persson A. The transluminal attenuation gradient does not add diagnostic accuracy to coronary computed tomography. Acta Radiol 2021; 62:867-874. [PMID: 32722968 DOI: 10.1177/0284185120943042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND A method for improving the accuracy of coronary computed tomography angiography (CCTA) is highly sought after as it would help to avoid unnecessary invasive coronary angiographies. Measurement of the transluminal attenuation gradient (TAG) has been proposed as an alternative to other existing methods, i.e. CT perfusion and CT fractional flow reserve (FFR). PURPOSE To evaluate the incremental value of three types of TAG in high-pitch spiral CCTA with invasive FFR measurements as reference. MATERIAL AND METHODS TAG was measured using two semi-automatic methods and one manual method. A receiver operating characteristic (ROC) analysis was made to determine the usefulness of TAG alone as well as TAG combined with CCTA for detection of significant coronary artery stenoses defined by an invasive FFR value ≤0.80. RESULTS A total of 51 coronary vessels in 37 patients were included in this retrospective study. Hemodynamically significant stenoses were found in 13 vessels according to FFR. The ROC analysis TAG alone resulted in areas under the curve (AUCs) of 0.530 and 0.520 for the semi-automatic TAG and 0.557 for the manual TAG. TAG and CCTA combined resulted in AUCs of 0.567, 0.562 for semi-automatic TAG, and 0.569 for the manual TAG. CONCLUSION The results from our study showed no incremental value of TAG measured in single heartbeat CCTA in determining the severity of coronary artery stenosis degrees.
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Affiliation(s)
- Lilian Henriksson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Radiology, Department of Health, Medicine and Caring Sciences and, Linköping University, Linköping, Sweden
| | - Mischa Woisetschläger
- Department of Radiology, Department of Health, Medicine and Caring Sciences and, Linköping University, Linköping, Sweden
| | - Joakim Alfredsson
- Department of Cardiology and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Magnus Janzon
- Department of Cardiology and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Jan Engvall
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Clinical Physiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Anders Persson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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Zhang ZZ, Guo Y, Hou Y. Artificial intelligence in coronary computed tomography angiography. Artif Intell Med Imaging 2021; 2:73-85. [DOI: 10.35711/aimi.v2.i3.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/20/2021] [Accepted: 07/02/2021] [Indexed: 02/06/2023] Open
Affiliation(s)
- Zhe-Zhe Zhang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Yan Guo
- GE Healthcare, Beijing 100176, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
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Liu CY, Tang CX, Zhang XL, Chen S, Xie Y, Zhang XY, Qiao HY, Zhou CS, Xu PP, Lu MJ, Li JH, Lu GM, Zhang LJ. Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality. Eur J Radiol 2021; 142:109835. [PMID: 34237493 DOI: 10.1016/j.ejrad.2021.109835] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/28/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To investigate the effect of reader experience, calcification and image quality on the performance of deep learning (DL) powered coronary CT angiography (CCTA) in automatically detecting obstructive coronary artery disease (CAD) with invasive coronary angiography (ICA) as reference standard. METHODS A total of 165 patients (680 vessels and 1505 segments) were included in this study. Three sessions were performed in order: (1) The artificial intelligence (AI) software automatically processed CCTA images, stenosis degree and processing time were recorded for each case; (2) Six cardiovascular radiologists with different experiences (low/ intermediate/ high experience) independently performed image post-processing and interpretation of CCTA, (3) AI + human reading was performed. Luminal stenosis ≥50% was defined as obstructive CAD in ICA and CCTA. Diagnostic performances of AI, human reading and AI + human reading were evaluated and compared on a per-patient, per-vessel and per-segment basis with ICA as reference standard. The effects of calcification and image quality on the diagnostic performance were also studied. RESULTS The average post-processing and interpretation times of AI was 2.3 ± 0.6 min per case, reduced by 76%, 72%, 69% compared with low/ intermediate/ high experience readers (all P < 0.001), respectively. On a per-patient, per-vessel and per-segment basis, with ICA as reference method, the AI overall diagnostic sensitivity for detecting obstructive CAD were 90.5%, 81.4%, 72.9%, the specificity was 82.3%, 93.9%, 95.0%, with the corresponding areas under the curve (AUCs) of 0.90, 0.90, 0.87, respectively. Compared to human readers, the diagnostic performance of AI was higher than that of low experience readers (all P < 0.001). The diagnostic performance of AI + human reading was higher than human reading alone, and AI + human readers' ability to correctly reclassify obstructive CAD was also improved, especially for low experience readers (Per-patient, the net reclassification improvement (NRI) = 0.085; per-vessel, NRI = 0.070; and per-segment, NRI = 0.068, all P < 0.001). The diagnostic performance of AI was not significantly affected by calcification and image quality (all P > 0.05). CONCLUSIONS AI can substantially shorten the post-processing time, while AI + human reading model can significantly improve the diagnostic performance compared with human readers, especially for inexperienced readers, regardless of calcification severity and image quality.
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Affiliation(s)
- Chun Yu Liu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Chun Xiang Tang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Xiao Lei Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Sui Chen
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Yuan Xie
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Xin Yuan Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Hong Yan Qiao
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Chang Sheng Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Peng Peng Xu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Meng Jie Lu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Jian Hua Li
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Guang Ming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China
| | - Long Jiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China.
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Variation of computed tomographic angiography-based fractional flow reserve after transcatheter aortic valve implantation. Eur Radiol 2021; 31:6220-6229. [PMID: 34156556 DOI: 10.1007/s00330-021-08099-y] [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: 02/21/2021] [Revised: 04/19/2021] [Accepted: 05/26/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES We sought to identify the impact of transcatheter aortic valve implantation (TAVI) on changes of fractional flow reserve computed tomography (FFRCT) values and the associated clinical impact. METHODS A retrospective analysis was done with CT obtained pre-TAVI, prior to hospital discharge and at 1-year follow-up, which provided imaging sources for the calculation of FFRCT values based on an online platform. RESULTS A total of 190 patients were enrolled. Patients with pre-procedural FFRCT value > 0.80 (i.e., negative) and ≤ 0.80 (i.e., positive) demonstrated a significantly opposite change in the value after TAVI (0.8798 vs. 0.8718, p < 0.001 and 0.7634 vs. 0.8222, p < 0.001, respectively). The history of coronary artery disease (CAD) was identified as an independent predictor for FFRCT changing from negative to positive after TAVI (odds ratio [OR] 2.927, 95% confidence interval [CI] 1.130-7.587, p = 0.027), with lesions more severely stenosed (OR 1.039, 95% CI 1.003-1.076, p = 0.034) and in left anterior descending coronary artery (LAD) (OR 3.939, 95% CI 1.060-14.637, p = 0.041) being prone to change. CONCLUSIONS TAVI directly brings improvement in FFRCT values in patients with compromised coronary flow. Patients with a history of CAD, especially with lesions more severely stenosed and in LAD, were under risk of FFRCT changing from negative to positive after TAVI. KEY POINTS •The effect of TAVI on coronary hemodynamics might be influenced by different ischemic severity and coronary territories reflected by FFRCT values. •As different FFRCT variations did not impact outcomes of TAVI patients, AS, but not coronary issues, may be the primary problem to affect, which needs further validation.
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50
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Renker M, Baumann S, Hamm CW, Tesche C, Kim WK, Savage RH, Coenen A, Nieman K, De Geer J, Persson A, Kruk M, Kepka C, Yang DH, Schoepf UJ. Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography. J Cardiovasc Comput Tomogr 2021; 15:492-498. [PMID: 34119471 DOI: 10.1016/j.jcct.2021.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/13/2021] [Accepted: 05/31/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort. METHODS Three hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments. RESULTS ML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%-79.5%] vs. 54.8% [45.7%-63.8%]), LAD (79.3 [73.9-84.0] vs. 59.6 [53.5-65.6]), LCX (84.1 [76.0-90.3] vs. 63.7 [54.1-72.6]), proximal (81.5 [74.6-87.1] vs. 63.8 [55.9-71.2]), middle (81.2 [75.7-85.9] vs. 59.4 [53.0-65.6]) and distal stenosis location (67.4 [57.0-76.6] vs. 51.6 [41.1-62.0]). CONCLUSION In a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.
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Affiliation(s)
- Matthias Renker
- Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Department of Cardiology, Campus Kerckhoff of Justus-Liebig-University Giessen, Bad Nauheim, Germany
| | - Stefan Baumann
- Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany
| | - Christian W Hamm
- Department of Cardiology, Campus Kerckhoff of Justus-Liebig-University Giessen, Bad Nauheim, Germany
| | - Christian Tesche
- Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Department of Internal Medicine I, St.-Johannes-Hospital, Dortmund, Germany
| | - Won-Keun Kim
- Department of Cardiology, Campus Kerckhoff of Justus-Liebig-University Giessen, Bad Nauheim, Germany
| | - Rock H Savage
- Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA
| | - Adriaan Coenen
- Department of Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Koen Nieman
- Department of Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jakob De Geer
- Department of Radiology and Department of Medical and Health Sciences, Center for Medical Image Science and Visualization, CMIV, Linköping University, Linköping, Sweden
| | - Anders Persson
- Department of Radiology and Department of Medical and Health Sciences, Center for Medical Image Science and Visualization, CMIV, Linköping University, Linköping, Sweden
| | - Mariusz Kruk
- Coronary Disease and Structural Heart Diseases Department, Invasive Cardiology and Angiology Department, Institute of Cardiology, Warsaw, Poland
| | - Cezary Kepka
- Coronary Disease and Structural Heart Diseases Department, Invasive Cardiology and Angiology Department, Institute of Cardiology, Warsaw, Poland
| | - Dong Hyun Yang
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - U Joseph Schoepf
- Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA.
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