1
|
Osborne-Grinter M, Ali A, Williams MC. Prevalence and clinical implications of coronary artery calcium scoring on non-gated thoracic computed tomography: a systematic review and meta-analysis. Eur Radiol 2024; 34:4459-4474. [PMID: 38133672 PMCID: PMC11213779 DOI: 10.1007/s00330-023-10439-z] [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: 05/02/2023] [Revised: 08/02/2023] [Accepted: 09/07/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVES Coronary artery calcifications (CACs) indicate the presence of coronary artery disease. CAC can be found on thoracic computed tomography (CT) conducted for non-cardiac reasons. This systematic review and meta-analysis of non-gated thoracic CT aims to assess the clinical impact and prevalence of CAC. METHODS Online databases were searched for articles assessing prevalence, demographic characteristics, accuracy and prognosis of incidental CAC on non-gated thoracic CT. Meta-analysis was performed using a random effects model. RESULTS A total of 108 studies (113,406 patients) were included (38% female). Prevalence of CAC ranged from 2.7 to 100% (pooled prevalence 52%, 95% confidence interval [CI] 46-58%). Patients with CAC were older (pooled standardised mean difference 0.88, 95% CI 0.65-1.11, p < 0.001), and more likely to be male (pooled odds ratio [OR] 1.95, 95% CI 1.55-2.45, p < 0.001), with diabetes (pooled OR 2.63, 95% CI 1.95-3.54, p < 0.001), hypercholesterolaemia (pooled OR 2.28, 95% CI 1.33-3.93, p < 0.01) and hypertension (pooled OR 3.89, 95% CI 2.26-6.70, p < 0.001), but not higher body mass index or smoking. Non-gated CT assessment of CAC had excellent agreement with electrocardiogram-gated CT (pooled correlation coefficient 0.96, 95% CI 0.92-0.98, p < 0.001). In 51,582 patients, followed-up for 51.6 ± 27.4 months, patients with CAC had increased all cause mortality (pooled relative risk [RR] 2.13, 95% CI 1.57-2.90, p = 0.004) and major adverse cardiovascular events (pooled RR 2.91, 95% CI 2.26-3.93, p < 0.001). When CAC was present on CT, it was reported in between 18.6% and 93% of reports. CONCLUSION CAC is a common, but underreported, finding on non-gated CT with important prognostic implications. CLINICAL RELEVANCE STATEMENT Coronary artery calcium is an important prognostic indicator of cardiovascular disease. It can be assessed on non-gated thoracic CT and is a commonly underreported finding. This represents a significant population where there is a potential missed opportunity for lifestyle modification recommendations and preventative therapies. This study aims to highlight the importance of reporting incidental coronary artery calcium on non-gated thoracic CT. KEY POINTS • Coronary artery calcification is a common finding on non-gated thoracic CT and can be reliably identified compared to gated-CT. • Coronary artery calcification on thoracic CT is associated with an increased risk of all cause mortality and major adverse cardiovascsular events. • Coronary artery calcification is frequently not reported on non-gated thoracic CT.
Collapse
Affiliation(s)
- Maia Osborne-Grinter
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
- University of Bristol, Bristol, UK.
| | - Adnan Ali
- School of Medicine, University of Dundee, Dundee, UK
| | - Michelle C Williams
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
2
|
Malik RF, Sun KJ, Azadi JR, Lau BD, Whelton S, Arbab-Zadeh A, Wilson RF, Johnson PT. Opportunistic Screening for Coronary Artery Disease: An Untapped Population Health Resource. J Am Coll Radiol 2024; 21:880-889. [PMID: 38382860 DOI: 10.1016/j.jacr.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 01/31/2024] [Accepted: 02/13/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Coronary artery disease is the leading cause of death in the United States. At-risk asymptomatic adults are eligible for screening with electrocardiogram-gated coronary artery calcium (CAC) CT, which aids in risk stratification and management decision-making. Incidental CAC (iCAC) is easily quantified on chest CT in patients imaged for noncardiac indications; however, radiologists do not routinely report the finding. OBJECTIVE To determine the clinical significance of CAC identified incidentally on routine chest CT performed for noncardiac indications. DESIGN An informationist developed search strategies in MEDLINE, Embase, and SCOPUS, and two reviewers independently screened results at both the abstract and full text levels. Data extracted from eligible articles included age, rate of iCAC identification, radiologist reporting frequency, impact on downstream medical management, and association of iCAC with patient outcomes. RESULTS From 359 unique citations, 83 research publications met inclusion criteria. The percentage of patients with iCAC ranged from 9% to 100%. Thirty-one investigations measured association(s) between iCAC and cardiovascular morbidity and mortality, and 29 identified significant correlations, including nonfatal myocardial infarction, fatal myocardial infarction, major adverse cardiovascular event, cardiovascular death, and all-cause death. iCAC was present in 20% to 100% of the patients in these cohorts, but when present, iCAC was reported by radiologists in only 31% to 44% of cases. Between 18% and 77% of patients with iCAC were not on preventive medications in studies that reported these data. Seven studies measured the effect of reporting on guideline directed medical therapy, and 5 (71%) reported an increase in medication prescriptions after diagnosis of iCAC, with one confirming reductions in low-density lipoprotein levels. Twelve investigations reported good concordance between CAC grade on noncardiac CT and Agatston score on electrocardiogram-gated cardiac CT, and 10 demonstrated that artificial intelligence tools can reliably calculate an Agatston score on noncardiac CT. CONCLUSION A body of evidence demonstrates that patients with iCAC on routine chest CT are at risk for cardiovascular disease events and death, but they are often undiagnosed. Uniform reporting of iCAC in the chest CT impression represents an opportunity for radiology to contribute to early identification of high-risk individuals and potentially reduce morbidity and mortality. AI tools have been validated to calculate Agatston score on routine chest CT and hold the best potential for facilitating broad adoption.
Collapse
Affiliation(s)
- Rubab F Malik
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kristie J Sun
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Javad R Azadi
- Assistant Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Brandyn D Lau
- Assistant Professor of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Seamus Whelton
- Associate Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Armin Arbab-Zadeh
- Director of Cardiac CT, Professor of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Renee F Wilson
- Evidence Based Practice Center, Johns Hopkins University School of Public Health, Baltimore, Maryland
| | - Pamela T Johnson
- Vice President of Care Transformation, Vice Chair of Quality and Safety in Radiology, and Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| |
Collapse
|
3
|
Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness. Eur Radiol 2023; 33:1973-1981. [PMID: 36152039 DOI: 10.1007/s00330-022-09143-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/09/2022] [Accepted: 09/01/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate commercial deep learning-based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). METHODS This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. RESULTS CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, -244.0, 283.9] vs. 45.26 [-248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). CONCLUSIONS Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. KEY POINTS • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mm than with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mm than with LDCT2.5-mm.
Collapse
|
4
|
Yang P, Zhao R, Deng W, An S, Li Y, Sheng M, Chen X, Qian Y, Yu Y, Mu D, Wang Y, Li X. Feasibility and accuracy of coronary artery calcium score on virtual non-contrast images derived from a dual-layer spectral detector CT: A retrospective multicenter study. Front Cardiovasc Med 2023; 10:1114058. [PMID: 36937907 PMCID: PMC10018184 DOI: 10.3389/fcvm.2023.1114058] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 02/10/2023] [Indexed: 03/06/2023] Open
Abstract
Rationale and objective This retrospective study was to evaluate the feasibility and accuracy of coronary artery calcium score (CACS) from virtual non-contrast (VNC) images in comparison with that from true non-contrast (TNC) images. Materials and methods A total of 540 patients with suspected of coronary artery disease (CAD) who underwent a dual-layer spectral detector CT (SDCT) in three hospitals were eligible for this study and 233 patients were retrospectively enrolled for further analysis. The CACS was calculated from both TNC and VNC images and compared. Linear regression analysis of the CACS was performed between TNC and VNC images. Results The correlation of overall CACS from VNC and TNC images was very strong (r = 0.923, p < 0.001). The CACS from VNC images were lower than that from TNC images (221 versus. 69, p < 0.001). When the regression equation of the overall coronary artery was applied, the mean calibrated CACS-VNC was 221 which had a significant difference from the CACS-TNC (p = 0.017). When the regression equation of each coronary branch artery was applied, the mean calibrated CACS-VNC was 221, which had a significant difference from the CACS-TNC (p = 0.003). But the mean difference between the CACS-TNC and the calibrated CACS-VNC in either way was less than 1. The agreement on risk stratification with CACS-TNC and CCACS-VNC was almost perfect. Conclusion This multicenter study with dual-layer spectral detector CT showed that it was feasible to calculate CACS from the VNC images derived from the spectral coronary artery CT angiography scan, and the results were in good accordance with the TNC images after correction. Therefore, the TNC scan could be omitted, reducing the radiation dose to patients and saving examination time while using dual-layer spectral detector CT.
Collapse
Affiliation(s)
- Panpan Yang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Ren Zhao
- Department of Cardiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Wei Deng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Shutian An
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Yuguo Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Mao Sheng
- Department of Radiology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Yingfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Dan Mu
- Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
- *Correspondence: Dan Mu, ; Yining Wang, ; Xiaohu Li,
| | - Yining Wang
- Department of Radiology, Peking Union Medical College Hospital, Beijing, China
- *Correspondence: Dan Mu, ; Yining Wang, ; Xiaohu Li,
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
- *Correspondence: Dan Mu, ; Yining Wang, ; Xiaohu Li,
| |
Collapse
|
5
|
Sartoretti E, Gennari AG, Maurer A, Sartoretti T, Skawran S, Schwyzer M, Rossi A, Giannopoulos AA, Buechel RR, Gebhard C, Huellner MW, Messerli M. Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT. Sci Rep 2022; 12:19191. [PMID: 36357446 PMCID: PMC9649723 DOI: 10.1038/s41598-022-20005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/07/2022] [Indexed: 11/11/2022] Open
Abstract
Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent oncological 18F-FDG-PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on dedicated non-contrast ECG-gated CT scans obtained from SPECT-MPI (reference standard). Additionally, CACS was performed fully automatically with a user-independent DL-CACS tool on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. Image quality and noise of CT scans was assessed. Agatston scores obtained by manual CACS and DL tool were compared. The high CACS group had Agatston scores of 2200 ± 1620 (reference standard) and 1300 ± 1011 (DL tool, average underestimation of 38.6 ± 26%) with an intraclass correlation of 0.714 (95% CI 0.546, 0.827). Sufficient image quality significantly improved the DL tool's capability of correctly assigning Agatston scores ≥ 1000 (p = 0.01). In the control group, the DL tool correctly assigned Agatston scores < 1000 in all cases. In conclusion, DL-based CACS performed on non-contrast free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations of patients with known very high (≥ 1000) CAC underestimates CAC load, but correctly assigns an Agatston scores ≥ 1000 in over 70% of cases, provided sufficient CT image quality. Subgroup analyses of the control group showed that the DL tool does not generate false-positives.
Collapse
Affiliation(s)
- Elisabeth Sartoretti
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Antonio G. Gennari
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Alexander Maurer
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Thomas Sartoretti
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Stephan Skawran
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Moritz Schwyzer
- grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.412004.30000 0004 0478 9977Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland ,grid.5801.c0000 0001 2156 2780Health Sciences and Technology, Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Alexia Rossi
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Andreas A. Giannopoulos
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Ronny R. Buechel
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Catherine Gebhard
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.7400.30000 0004 1937 0650Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland
| | - Martin W. Huellner
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| |
Collapse
|
6
|
Morf C, Sartoretti T, Gennari AG, Maurer A, Skawran S, Giannopoulos AA, Sartoretti E, Schwyzer M, Curioni-Fontecedro A, Gebhard C, Buechel RR, Kaufmann PA, Huellner MW, Messerli M. Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT. Diagnostics (Basel) 2022; 12:diagnostics12081876. [PMID: 36010226 PMCID: PMC9406755 DOI: 10.3390/diagnostics12081876] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/24/2022] [Accepted: 07/27/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives: The objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT. Methods: A total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared. Results: On a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery. Conclusions: Fully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings.
Collapse
Affiliation(s)
- Claudia Morf
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Antonio G. Gennari
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Andreas A. Giannopoulos
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Elisabeth Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Moritz Schwyzer
- University of Zurich, 8006 Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland;
| | - Alessandra Curioni-Fontecedro
- University of Zurich, 8006 Zurich, Switzerland
- Department of Medical Oncology and Hematology, University Hospital Zurich, 8091 Zurich, Switzerland;
| | - Catherine Gebhard
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
- Center for Molecular Cardiology, University of Zurich, 8006 Zurich, Switzerland
| | - Ronny R. Buechel
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Philipp A. Kaufmann
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Martin W. Huellner
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
- Correspondence:
| |
Collapse
|
7
|
Klug M, Shemesh J, Green M, Mayer A, Kerpel A, Konen E, Marom E. A deep-learning method for the denoising of ultra-low dose chest CT in coronary artery calcium score evaluation. Clin Radiol 2022; 77:e509-e517. [DOI: 10.1016/j.crad.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/11/2022] [Indexed: 11/03/2022]
|
8
|
An S, Fan R, Zhao B, Yi Q, Yao S, Shi X, Zhu Y, Yi X, Liu S. Evaluating coronary artery calcification with low-dose chest CT reconstructed by different kernels. Clin Imaging 2022; 83:166-171. [DOI: 10.1016/j.clinimag.2021.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/22/2021] [Accepted: 12/29/2021] [Indexed: 11/25/2022]
|
9
|
Xu J, Liu J, Guo N, Chen L, Song W, Guo D, Zhang Y, Fang Z. Performance of artificial intelligence-based coronary artery calcium scoring in non-gated chest CT. Eur J Radiol 2021; 145:110034. [PMID: 34837795 DOI: 10.1016/j.ejrad.2021.110034] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/17/2021] [Accepted: 10/22/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the risk category performance of artificial intelligence-based coronary artery calcium score (AI-CACS) software used in non-gated chest computed tomography (CT) on three types of CT machines, considering the manual method as the standard. METHODS A total of 901 patients who underwent both chest CT and electrocardiogram (ECG)-gated non-contrast-enhanced cardiac CT with the same equipment within a 3-month period were enrolled in the study. AI-CACS software was based on a deep learning algorithm and was trained on multi-vendor, multi-scanner, and multi-hospital anonymized data from the chest CT database. The AI-CACS was automatically obtained from chest CT data by the AI-CACS software, while the manual CACS was obtained from cardiac CT data by the manual method. The correlation of the AI-CACS and manual CACS, concordance rate and kappa value of the risk categories determined by the two methods were calculated. The chi-square test was used to evaluate the differences in risk categories among the three types of CT machines from different manufacturers. The risk category performance of the AI-CACS for dichotomous risk categories bounded by 0, 100 and 400 was assessed. RESULTS The correlation of the AI-CACS with the manual CACS was ρ = 0.893 (p < 0.001). The Bland-Altman plot (AI-CACS minus manual CACS) showed a mean difference of -27.2 and 95% limits of agreement of -290.0 to 235.6. The agreement of risk categories for the CACS was kappa (κ) = 0.679 (p < 0.001), and the concordance rate was 80.6%. The risk categories determined by the AI-CACS software on three types of CT machines were not significantly different (p = 0.7543). As dichotomous risk categories bounded by 0, 100 and 400, the accuracy, kappa value, and area under the curve of the AI-CACS were 88.6% vs. 92.9% vs. 97.9%, 0.77 vs. 0.77 vs. 0.83, and 0.885 vs. 0.964 vs. 0.981, respectively. CONCLUSIONS There was good correlation and agreement between the AI-CACS and manual CACS in terms of the risk category. It is feasible to obtain the CACS using AI software based on non-gated chest CT data in a short time without increasing the radiation dose or economic burden. The AI-CACS software algorithm has good clinical universality and can be applied to CT machines from different manufacturers.
Collapse
Affiliation(s)
- Jie Xu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Jia Liu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Ning Guo
- ShuKun (BeiJing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, 100000 Beijing, China
| | - Linli Chen
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Weixiang Song
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Yu Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China.
| | - Zheng Fang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China.
| |
Collapse
|
10
|
Kim JY, Suh YJ, Han K, Choi BW. Reliability of Coronary Artery Calcium Severity Assessment on Non-Electrocardiogram-Gated CT: A Meta-Analysis. Korean J Radiol 2021; 22:1034-1043. [PMID: 33856134 PMCID: PMC8236368 DOI: 10.3348/kjr.2020.1047] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/09/2020] [Accepted: 12/01/2020] [Indexed: 02/01/2023] Open
Abstract
OBJECTIVE The purpose of this meta-analysis was to investigate the pooled agreements of the coronary artery calcium (CAC) severities assessed by electrocardiogram (ECG)-gated and non-ECG-gated CT and evaluate the impact of the scan parameters. MATERIALS AND METHODS PubMed, EMBASE, and the Cochrane library were systematically searched. A modified Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to evaluate the quality of the studies. Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and the correlation coefficient of the CAC scores or the weighted kappa for the categorization of the CAC severities detected by the two modalities. The heterogeneity among the studies was also assessed. Subgroup analyses were performed based on factors that could affect the measurement of the CAC score and severity: slice thickness, reconstruction kernel, and radiation dose for non-ECG-gated CT. RESULTS A total of 4000 patients from 16 studies were included. The pooled bias was 62.60, 95% LOA were -36.19 to 161.40, and the pooled correlation coefficient was 0.94 (95% confidence interval [CI] = 0.89-0.97) for the CAC score. The pooled weighted kappa of the CAC severity was 0.85 (95% CI = 0.79-0.91). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, the agreement between the CAC categorizations was better when the two CT examinations had reconstructions based on the same slice thickness and kernel. CONCLUSION The pooled agreement of the CAC severities assessed by the ECG-gated and non-ECG-gated CT was excellent; however, it was significantly affected by scan parameters, such as slice thickness and the reconstruction kernel.
Collapse
Affiliation(s)
- Jin Young Kim
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Young Joo Suh
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Byoung Wook Choi
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
11
|
Pan YK, Sun MH, Wang JJ, Chen XB, Kan XJ, Ge YH, Guo ZP. Effect of different reconstruction algorithms on coronary artery calcium scores using the reduced radiation dose protocol: a clinical and phantom study. Quant Imaging Med Surg 2021; 11:1504-1517. [PMID: 33816187 DOI: 10.21037/qims-20-437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background This study aimed to evaluate the effects of different iterative reconstruction (IR) algorithms on coronary artery calcium (CAC) score quantification using the reduced radiation dose (RRD) protocol in an anthropomorphic phantom and in patients. Methods A thorax phantom, containing 9 calcification inserts with varying hydroxyapatite (HA) densities, was scanned with the reference protocol [120 kv, 80 mAs, filtered back projection (FBP)] and RRD protocol (120 kV, 20-80 mAs, 5 mAs interval) using a 256-slice computed tomography (CT) scanner. Raw data were reconstructed with different reconstruction algorithms [iDose4 levels 1-7 and iterative model reconstruction (IMR) levels 1-3]. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Agatston score (AS) were calculated for each image series. The correction factor was derived from linear regression analysis between the reference image series and other image series with different parameters. Additionally, 40 patients were scanned with the RRD protocol (50 mAs) and reconstructed with FBP, iDose4 level 4, and IMR level 2. AS was calculated for the 3-group image series, and was corrected by applying a correction factor for the IMR group. The agreement of risk stratification with different reconstruction algorithms was also analyzed. Results For the phantom study, the iDose4 and IMR groups had significantly higher SNR and CNR than the FBP group (all P<0.05). There were no significant differences in the total AS after comparing image series reconstructed with iDose4 (level 1-7) and FBP (all P>0.05), while AS from the IMR (level 1-3) image series were lower than the FBP group (all P<0.05). The tube current of 50 mAs was determined for the clinical study, and the correction factor was 1.14. For the clinical study, the median AS from the iDose4 and IMR groups were both significantly lower compared to the FBP image series [(112.89 (63.01, 314.09), 113.22 (64.78, 364.95) vs. 118.59 (65.05, 374.48), both P<0.05]. After applying the correction factor, the adjusted AS from the IMR group was not significantly different from that of the FBP group [126.48 (69.62, 355.85) vs. 118.59 (65.05, 374.48), P=0.145]. Moreover, the agreement in risk stratification between FBP and IMR improved from 0.81 to 0.85. Conclusions The RRD CAC scoring scan using the IMR reconstruction algorithm is clinically feasible, and a correction factor can help reduce the AS underestimation effect.
Collapse
Affiliation(s)
- Yu-Kun Pan
- Department of Radiology, Central China Fuwai Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming-Hua Sun
- Department of Radiology, Henan Provincial People's Hospital, Department of Radiology of Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China
| | - Jia-Jia Wang
- Department of Radiology, Central China Fuwai Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | | | - Xiao-Jing Kan
- Department of Radiology, Henan Provincial People's Hospital, Department of Radiology of Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China
| | - Ying-Hui Ge
- Department of Radiology, Henan Provincial People's Hospital, Department of Radiology of Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China
| | | |
Collapse
|
12
|
Waltz J, Kocher M, Kahn J, Dirr M, Burt JR. The Future of Concurrent Automated Coronary Artery Calcium Scoring on Screening Low-Dose Computed Tomography. Cureus 2020; 12:e8574. [PMID: 32670710 PMCID: PMC7358941 DOI: 10.7759/cureus.8574] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 06/11/2020] [Indexed: 12/19/2022] Open
Abstract
Low-dose computed tomography (LDCT) has been extensively validated for lung cancer screening in selected patient populations. Additionally, the use of gated cardiac CT to assess coronary artery calcium (CAC) burden has been validated to determine a patient's risk for major cardiovascular adverse events. This is typically performed by calculating an Agatston score based on density and overall burden of calcified plaque within the coronary arteries. Patients that qualify for LDCT for lung cancer screening commonly share major risk factors for coronary artery disease and would frequently benefit from an additional gated cardiac CT for the assessment of CAC. Given the widespread use of LDCT for lung cancer screening, we evaluated current literature regarding the use of non-gated chest CT, specifically LDCT, for the detection and grading of coronary artery calcifications. Additionally, given the evolving and increasing use of artificial intelligence (AI) in the interpretation of radiologic studies, current literature for the use of AI in CAC assessment was reviewed. We reviewed primary scientific literature dating up to April 2020 using Pubmed and Google Scholar, with the search terms low dose CT, lung cancer screening, coronary artery calcium, EKG/cardiac gated CT, deep learning, machine learning, and AI. These publications were then independently evaluated by each member of our team. Overall, there was a consensus within these papers that LDCT for lung cancer screening plays a role in the evaluation of CAC. Most studies note the inherent problems with the evaluation of the density of coronary calcifications on LDCT to give an accurate numeric calcium or Agatston score. The current method of evaluating CAC on LDCT involves using a qualitative categorical system (none, mild, moderate, or severe). When performed by cardiac imaging experts, this method broadly correlates with traditional CAC score groups (0, 1 to 100, 101 to 400, and > 400). Furthermore, given the high sensitivity of a properly protocolled LDCT for coronary calcium, a negative study for CAC precludes the need for a dedicated gated CT assessment. However, qualitative methods are not as accurate or reproducible when performed by general radiologists. The implementation of AI in the LDCT screening process has the potential to give a quantifiable and reproducible numeric value to the calcium score, based on whole heart volume scoring of calcium. This more closely aligns with the Agatston score and serves as a better guide for treatment and risk assessment using current guidelines. We conclude that CAC should be assessed on all LDCT performed for lung cancer screening and that a qualitative categorical scoring system should be provided in the impression for each patient. Early studies involving AI for the assessment of CAC are promising, but more extensive studies are needed before a final recommendation for its use can be given. The implementation of an accurate, automated AI CAC assessment tool would improve radiologist compliance and ease of overall workflow. Ultimately, the potential end result would be improved turnaround time, better patient outcomes, and reduced healthcare costs by maximizing preventative care in this high-risk population.
Collapse
Affiliation(s)
- Jeffrey Waltz
- Diagnostic Radiology, Medical University of South Carolina, Charleston, USA
| | - Madison Kocher
- Radiology, Medical University of South Carolina, Charleston, USA
| | - Jacob Kahn
- Radiology, Medical University of South Carolina, Charleston, USA
| | - McKenzie Dirr
- Radiology, Medical University of South Carolina, Charleston, USA
| | - Jeremy R Burt
- Radiology, Medical University of South Carolina, Charleston, USA
- Cardiothoracic Imaging, Medical University of South Carolina, Charleston, USA
| |
Collapse
|