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Bonney A, Chua M, McCusker MW, Pascoe D, Joshi SB, Steinfort D, Marshall H, Silver JD, Xie C, Yang S, Watson J, Fogarty P, Stone E, Brims F, McWilliams A, Hu X, Rofe C, Milner B, Lam S, Fong KM, Manser R. Coronary artery calcification detected on low-dose computed tomography in high-risk participants of an Australian lung cancer screening program: A prospective observational study. Respirology 2025; 30:62-69. [PMID: 39318183 DOI: 10.1111/resp.14832] [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: 04/18/2024] [Accepted: 09/11/2024] [Indexed: 09/26/2024]
Abstract
BACKGROUND AND OBJECTIVES Coronary artery calcification (CAC) is a frequent additional finding on lung cancer screening (LCS) low-dose computed tomography (LDCT). Cardiovascular disease (CVD) is a major cause of death in LCS participants. We aimed to describe prevalence of incidental CAC detected on LDCT in LCS participants without prior history of coronary artery disease (CAD), evaluate their CVD risk and describe subsequent investigation and management. METHODS Prospective observational nested cohort study including all participants enrolled at a single Australian site of the International Lung Screen Trial. Baseline LDCTs were reviewed for CAC, and subsequent information collected regarding cardiovascular health. 5-year CVD risk was calculated using the AusCVD risk calculator. RESULTS 55% (226/408) of participants had CAC on LDCT and no prior history of CAD, including 23% with moderate-severe CAC. Mean age of participants with CAC was 65 years, 68% were male. 53% were currently smoking. Majority were high risk (51%) or intermediate risk (32%) of a cardiovascular event in 5 years. 21% of participants were re-stratified to a higher CVD risk group when CAC detected on LCS was incorporated. Only 10% of participants with CAC received lifestyle advice (only 3% currently smoking received smoking cessation advice). 80% of participants at high-risk did not meet guideline recommendations, with 47% of this group remaining without cholesterol lowering therapy. CONCLUSION LCS with LDCT offers the potential to identify and communicate CVD risk in this population. This may improve health outcomes for high-risk LCS participants and further personalize management once screening results are known.
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Affiliation(s)
- Asha Bonney
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Michelle Chua
- Department of Radiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Mark W McCusker
- Department of Radiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Radiology, University of Melbourne, Melbourne, Victoria, Australia
| | - Diane Pascoe
- Department of Radiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Radiology, University of Melbourne, Melbourne, Victoria, Australia
| | - Subodh B Joshi
- Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Daniel Steinfort
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Henry Marshall
- Thoracic Research Centre, University of Queensland, Chermside, Queensland, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Chermside, Queensland, Australia
| | - Jeremy D Silver
- Statistical Consulting Centre, School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Cheng Xie
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Sally Yang
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Jack Watson
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Paul Fogarty
- Respiratory Department, Epworth Eastern Hospital, Box Hill, Victoria, Australia
| | - Emily Stone
- Department of Thoracic Medicine and Lung Transplantation, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
- University of New South Wales, School of Clinical Medicine, St Vincent's Clinical School; School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Fraser Brims
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- Curtin Medical School, Curtin University, Bentley, Western Australia, Australia
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, Nedlands, Western Australia, Australia
| | - Annette McWilliams
- Department of Respiratory Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
- Department of Medicine, University of Western Australia, Nedlands, Western Australia, Australia
| | - XinXin Hu
- Department of Thoracic Medicine and Lung Transplantation, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
| | - Christopher Rofe
- Department of Thoracic Medicine and Lung Transplantation, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
| | - Brad Milner
- Department of Thoracic Medicine and Lung Transplantation, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
| | - Stephen Lam
- Department of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Kwun M Fong
- Thoracic Research Centre, University of Queensland, Chermside, Queensland, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Chermside, Queensland, Australia
| | - Renee Manser
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
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DeSantis W, Ayoade O, Caturegli G, Boffa DJ. Lung Cancer Screening at US Hospitals for People Lacking Primary Care. JAMA Netw Open 2024; 7:e2442373. [PMID: 39480427 PMCID: PMC11528307 DOI: 10.1001/jamanetworkopen.2024.42373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2024] [Accepted: 09/09/2024] [Indexed: 11/03/2024] Open
Abstract
This quality improvement study investigates how many US hospitals allow patients to schedule lung cancer screenings without a referral from a primary care practitioner.
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Kunitomo Y, Sather P, Killam J, Pisani MA, Slade MD, Tanoue LT. Impact of Structured Reporting For Lung Cancer Screening Low-Dose CT Scan Incidental Findings on Physician Management. Chest 2024; 166:896-898. [PMID: 38346557 DOI: 10.1016/j.chest.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Affiliation(s)
- Yukiko Kunitomo
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Polly Sather
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Jonathan Killam
- Department of Radiology, Geisel School of Medicine at Dartmouth, Hanover, NH
| | - Margaret A Pisani
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Martin D Slade
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Lynn T Tanoue
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
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Marcinkiewicz AM, Buchwald M, Shanbhag A, Bednarski BP, Killekar A, Miller RJ, Builoff V, Lemley M, Berman DS, Dey D, Slomka PJ, Weintraub E. AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening. Radiology 2024; 312:e240541. [PMID: 39287522 PMCID: PMC11427857 DOI: 10.1148/radiol.240541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 07/01/2024] [Accepted: 07/10/2024] [Indexed: 09/19/2024]
Abstract
Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ2 test and Kruskal-Wallis rank sum test were used for statistical analyses. Results A total of 24 401 participants (median age, 61 years [IQR, 57-65 years]; 14 468 male) were included. In 3880 of 24 401 participants (16%), 4283 extrapulmonary significant incidental findings were reported. During the 10-year follow-up, 3389 of 24 401 participants (14%) died. CAC had the highest feature importance for predicting the three study end points. The 10-year ACM model demonstrated the best AUC performance (0.72; per-year mortality of 2.6% above and 0.8% below the risk threshold), followed by 2-year ACM (0.71; per-year mortality of 1.13% above and 0.3% below the risk threshold) and prediction of extrapulmonary significant incidental findings (0.70; probability of occurrence of 25.4% above and 9.6% below the threshold). Conclusion A fully automated AI model indicated extrapulmonary structures at risk on chest CT scans and predicted ACM with explanations. ClinicalTrials.gov Identifier: NCT00047385 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yanagawa and Hata in this issue.
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Affiliation(s)
- Anna M. Marcinkiewicz
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Mikolaj Buchwald
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Aakash Shanbhag
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Bryan P. Bednarski
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Aditya Killekar
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Robert J.H. Miller
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Valerie Builoff
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Mark Lemley
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Daniel S. Berman
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Damini Dey
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Piotr J. Slomka
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
| | - Elizabeth Weintraub
- From the Departments of Medicine, Division of Artificial Intelligence
in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500
Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K.,
R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing
Institute, Ming Hsieh Department of Electrical and Computer Engineering,
University of Southern California, Los Angeles, Calif (A.S.); and Department of
Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
(R.J.H.M.)
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5
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Lin Y, Khurelsukh K, Li IG, Wu CT, Wu YM, Lin G, Toh CH, Wan YL. Incidental Findings in Lung Cancer Screening. Cancers (Basel) 2024; 16:2600. [PMID: 39061238 PMCID: PMC11274500 DOI: 10.3390/cancers16142600] [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: 06/27/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024] Open
Abstract
While low-dose computed tomography (LDCT) for lung cancer screening (LCS) has been recognized for its effectiveness in reducing lung cancer mortality, it often simultaneously leads to the detection of incidental findings (IFs) unrelated to the primary screening indication. These IFs present diagnostic and management challenges, potentially causing unnecessary anxiety and further invasive diagnostic procedures for patients. This review article provides an overview of IFs encountered in LDCT, emphasizing their clinical significance and recommended management strategies. We categorize IFs based on their anatomical locations (intrathoracic-intrapulmonary, intrathoracic-extrapulmonary, and extrathoracic) and discuss the most common findings. We highlight the importance of utilizing guidelines and standardized reporting systems by the American College of Radiology (ACR) to guide appropriate follow-ups. For each category, we present specific IF examples, their radiologic features, and the suggested management approach. This review aims to provide radiologists and clinicians with a comprehensive understanding of IFs in LCS for accurate assessment and management, ultimately enhancing patient care. Finally, we outline a few key aspects for future research and development in managing IFs.
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Affiliation(s)
- Yenpo Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; (Y.L.); (K.K.); (C.-T.W.); (Y.-M.W.); (G.L.); (C.-H.T.)
| | - Khulan Khurelsukh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; (Y.L.); (K.K.); (C.-T.W.); (Y.-M.W.); (G.L.); (C.-H.T.)
| | - I-Gung Li
- Department of Medical Imaging and Intervention, New Taipei Municipal Tucheng Hospital, New Taipei City 236, Taiwan;
| | - Chen-Te Wu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; (Y.L.); (K.K.); (C.-T.W.); (Y.-M.W.); (G.L.); (C.-H.T.)
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan
| | - Yi-Ming Wu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; (Y.L.); (K.K.); (C.-T.W.); (Y.-M.W.); (G.L.); (C.-H.T.)
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; (Y.L.); (K.K.); (C.-T.W.); (Y.-M.W.); (G.L.); (C.-H.T.)
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan
| | - Cheng-Hong Toh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; (Y.L.); (K.K.); (C.-T.W.); (Y.-M.W.); (G.L.); (C.-H.T.)
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan
| | - Yung-Liang Wan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; (Y.L.); (K.K.); (C.-T.W.); (Y.-M.W.); (G.L.); (C.-H.T.)
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan
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Yang X, Du Y, Joost Wisselink H, Zhao Y, Heuvelmans MA, J M Groen H, Dorrius MD, Vonder M, Ye Z, Vliegenthart R, de Bock GH. Ct-defined emphysema prevalence in a Chinese and Dutch general population. Eur J Radiol 2024; 176:111503. [PMID: 38761443 DOI: 10.1016/j.ejrad.2024.111503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/19/2024] [Accepted: 05/12/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE We determine and compare the prevalence, subtypes, severity, and risk factors for emphysema assessed by low-dose CT(LDCT) in Chinese and Dutch general populations. METHODS This cross-sectional study included LDCT scans of 1143 participants between May and October 2017 from a Chinese Cohort study and 1200 participants with same age range and different smoking status between May and October 2019 from a Dutch population-based study. An experienced radiologist visually assessed the scans for emphysema presence (≥trace), subtype, and severity. Logistic regression analyses, overall and stratified by smoking status, were performed and adjusted for fume exposure, demographic and smoking data. RESULTS The Chinese population had a comparable proportion of women to the Dutch population (54.9 % vs 58.9 %), was older (61.7 ± 6.3 vs 59.8 ± 8.1), included more never smokers (66.4 % vs 38.3 %), had a higher emphysema prevalence ([58.8 % vs 39.7 %], adjusted odds ratio, aOR = 2.06, 95 %CI = 1.68-2.53), and more often had centrilobular emphysema (54.8 % vs 32.8 %, p < 0.001), but no differences in emphysema severity. After stratification, only in never smokers an increased odds of emphysema was observed in the Chinese compared to the Dutch (aOR = 2.55, 95 %CI = 1.95-3.35). Never smokers in both populations shared older age (aOR = 1.59, 95 %CI = 1.25-2.02 vs 1.26, 95 %CI = 0.97-1.64) and male sex (aOR = 1.50, 95 %CI = 1.02-2.22 vs 1.93, 95 %CI = 1.26-2.96) as risk factors for emphysema. CONCLUSIONS Only never smokers had a higher prevalence of mainly centrilobular emphysema in the Chinese general population compared to the Dutch after adjusting for confounders, indicating that factors other than smoking, age and sex contribute to presence of CT-defined emphysema.
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Affiliation(s)
- Xiaofei Yang
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Yihui Du
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Hendrik Joost Wisselink
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Yingru Zhao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Marjolein A Heuvelmans
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Harry J M Groen
- Department of Pulmonary Diseases, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Monique D Dorrius
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Marleen Vonder
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Geertruida H de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
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Park C, Lee BC, Jeong WG, Park WJ, Jin GY, Kim YH. Coronary Artery Calcification on Low-Dose Lung Cancer Screening CT in South Korea: Visual and Artificial Intelligence-Based Assessment and Association With Cardiovascular Events. AJR Am J Roentgenol 2024; 222:e2430852. [PMID: 38447024 DOI: 10.2214/ajr.24.30852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
BACKGROUND. Coronary artery calcification (CAC) on lung cancer screening low-dose chest CT (LDCT) is a cardiovascular risk marker. South Korea was the first Asian country to initiate a national LDCT lung cancer screening program, although CAC-related outcomes are poorly explored. OBJECTIVE. The purpose of this article is to evaluate CAC prevalence and severity using visual analysis and artificial intelligence (AI) methods and to characterize CAC's association with major adverse cardiovascular events (MACEs) in patients undergoing LDCT in Korea's national lung cancer screening program. METHODS. This retrospective study included 1002 patients (mean age, 62.4 ± 5.4 [SD] years; 994 men, eight women) who underwent LDCT at two Korean medical centers between April 2017 and May 2023 as part of Korea's national lung cancer screening program. Two radiologists independently assessed CAC presence and severity using visual analysis, consulting a third radiologist to resolve differences. Two AI software applications were also used to assess CAC presence and severity. MACE occurrences were identified by EMR review. RESULTS. Interreader agreement for CAC presence and severity, expressed as kappa, was 0.793 and 0.671, respectively. CAC prevalence was 53.4% by consensus visual assessment, 60.1% by AI software I, and 56.6% by AI software II. CAC severity was mild, moderate, and severe by consensus visual analysis in 28.0%, 10.3%, and 15.1%; by AI software I in 39.9%, 14.0%, and 6.2%; and by AI software II in 34.9%, 14.3%, and 7.3%. MACEs occurred in 36 of 625 (5.6%) patients with follow-up after LDCT (median, 1108 days). MACE incidence in patients with no, mild, moderate, and severe CAC for consensus visual analysis was 1.1%, 5.0%, 2.9%, and 8.6%, respectively (p < .001); for AI software I, it was 1.3%, 3.0%, 7.9%, and 11.3% (p < .001); and for AI software II, it was 1.2%, 3.4%, 7.7%, and 9.6% (p < .001). CONCLUSION. For Korea's national lung cancer screening program, MACE occurrence increased significantly with increasing CAC severity, whether assessed by visual analysis or AI software. The study is limited by the large sex imbalance for Korea's national lung cancer screening program. CLINICAL IMPACT. The findings provide reference data for health care practitioners engaged in developing and overseeing national lung cancer screening programs, highlighting the importance of routine CAC evaluation.
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Affiliation(s)
- Chan Park
- Department of Radiology, Chonnam National University Hospital and Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Byung Chan Lee
- Department of Radiology, Chonnam National University Hwasun Hospital and Chonnam National University Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeollanam-do, Republic of Korea 58128
| | - Won Gi Jeong
- Department of Radiology, Chonnam National University Hwasun Hospital and Chonnam National University Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeollanam-do, Republic of Korea 58128
| | - Won-Ju Park
- Department of Occupational and Environmental Medicine, Chonnam National University Medical School, Chonnam National University Hwasun Hospital, Hwasun-eup, Hwasun-gun, Jeollanam-do, Republic of Korea
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine, Jeonbuk National University-Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Yun-Hyeon Kim
- Department of Radiology, Chonnam National University Hospital and Chonnam National University Medical School, Gwangju, Republic of Korea
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8
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Hardavella G, Frille A, Chalela R, Sreter KB, Petersen RH, Novoa N, de Koning HJ. How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape? Eur Respir Rev 2024; 33:230232. [PMID: 38925794 PMCID: PMC11216686 DOI: 10.1183/16000617.0232-2023] [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: 11/09/2023] [Accepted: 04/16/2024] [Indexed: 06/28/2024] Open
Abstract
INTRODUCTION Implementation of lung cancer screening, with its subsequent findings, is anticipated to change the current diagnostic and surgical lung cancer landscape. This review aimed to identify and present the most updated expert opinion and discuss relevant evidence regarding the impact of lung cancer screening and lung nodule management on the diagnostic and surgical landscape of lung cancer, as well as summarise points for clinical practice. METHODS This article is based on relevant lectures and talks delivered during the European Society of Thoracic Surgeons-European Respiratory Society Collaborative Course on Thoracic Oncology (February 2023). Original lectures and talks and their relevant references were included. An additional literature search was conducted and peer-reviewed studies in English (December 2022 to June 2023) from the PubMed/Medline databases were evaluated with regards to immediate affinity of the published papers to the original talks presented at the course. An updated literature search was conducted (June 2023 to December 2023) to ensure that updated literature is included within this article. RESULTS Lung cancer screening suspicious findings are expected to increase the number of diagnostic investigations required therefore impacting on current capacity and resources. Healthcare systems already face a shortage of imaging and diagnostic slots and they are also challenged by the shortage of interventional radiologists. Thoracic surgery will be impacted by the wider lung cancer screening implementation with increased volume and earlier stages of lung cancer. Nonsuspicious findings reported at lung cancer screening will need attention and subsequent referrals where required to ensure participants are appropriately diagnosed and managed and that they are not lost within healthcare systems. CONCLUSIONS Implementation of lung cancer screening requires appropriate mapping of existing resources and infrastructure to ensure a tailored restructuring strategy to ensure that healthcare systems can meet the new needs.
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Affiliation(s)
- Georgia Hardavella
- 4th-9th Department of Respiratory Medicine, "Sotiria" Athens' Chest Diseases Hospital, Athens, Greece
| | - Armin Frille
- Department of Respiratory Medicine, University of Leipzig, Leipzig, Germany
| | - Roberto Chalela
- Department of Respiratory Medicine: Lung Cancer and Endoscopy Unit, Hospital del Mar - Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Katherina B Sreter
- Department of Pulmonology, University Hospital Centre "Sestre Milosrdnice", Zagreb, Croatia
| | - Rene H Petersen
- Department of Cardiothoracic Surgery, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Nuria Novoa
- Department of Thoracic Surgery, University Hospital Puerta de Hierro-Majadahonda, Madrid, Spain
| | - Harry J de Koning
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
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Kher S, Cheung A, Baron LS. Separating Actionable From Incidental Findings-Imperative for Meaningful Clinical Outcomes. JAMA Intern Med 2023; 183:1176. [PMID: 37639268 DOI: 10.1001/jamainternmed.2023.4070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Affiliation(s)
- Sucharita Kher
- Tufts University School of Medicine, Boston, Massachusetts
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Tufts Medical Center-Tufts Medicine, Boston, Massachusetts
| | - Arnold Cheung
- Tufts University School of Medicine, Boston, Massachusetts
- Melrose Wakefield Hospital-Tufts Medicine, Boston, Massachusetts
| | - Lindsay S Baron
- Tufts University School of Medicine, Boston, Massachusetts
- Lowell General Hospital-Tufts Medicine, Boston, Massachusetts
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10
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Gareen IF, Hoffman RM, Tailor TD. Separating Actionable From Incidental Findings-Imperative for Meaningful Clinical Outcomes-Reply. JAMA Intern Med 2023; 183:1176-1177. [PMID: 37639256 DOI: 10.1001/jamainternmed.2023.4067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Affiliation(s)
- Ilana F Gareen
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
| | - Richard M Hoffman
- Holden Comprehensive Cancer Center, Department of Medicine, University of Iowa Carver College of Medicine, University of Iowa, Iowa City
| | - Tina D Tailor
- Division of Cardiothoracic Radiology, Department of Radiology, Duke Health, Durham, North Carolina
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Patel P, Bradley SH, McCutchan G, Brain K, Redmond P. What should the role of primary care be in lung cancer screening? Br J Gen Pract 2023; 73:340-341. [PMID: 37500469 PMCID: PMC10405969 DOI: 10.3399/bjgp23x734397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023] Open
Affiliation(s)
- Priya Patel
- GKT School of Medical Education, King's College London, London, UK
| | - Stephen H Bradley
- GP and National Institute for Health and Care Research Academic Clinical Lecturer, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | | | - Kate Brain
- School of Medicine, Cardiff University, Cardiff, UK
| | - Patrick Redmond
- Department of General Practice, Royal College of Surgeons in Ireland, Dublin, Ireland
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