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Yuan J, Xu F, Sun Y, Ren H, Chen M, Feng S. Shared decision-making in the management of pulmonary nodules: a systematic review of quantitative and qualitative studies. BMJ Open 2024; 14:e079080. [PMID: 38991667 PMCID: PMC11243204 DOI: 10.1136/bmjopen-2023-079080] [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: 08/21/2023] [Accepted: 06/26/2024] [Indexed: 07/13/2024] Open
Abstract
OBJECTIVE The objective of this systematic review was to explore the evidence regarding shared decision-making (SDM) in the management of pulmonary nodules. DESIGN Systematic review of quantitative and qualitative studies. DATA SOURCE Studies published in English or Chinese up to April 2022 were extracted from nine databases: PubMed, PsycINFO, EMBASE, Cochrane Library, Web of Science and CINAHL, China National Knowledge Infrastructure, Wanfang Data and SinoMed Data. ELIGIBILITY CRITERIA Studies were eligible if patients or healthcare providers are faced with pulmonary nodule management options or the interventions or experiences were focused on the patient-healthcare provider relationship or health education to make, increase or support shared decisions. All types of studies were included, including quantitative and qualitative studies. Grey literature and literature that had not been peer reviewed were excluded. Poster abstracts and non-empirical publications such as editorials, letters, opinion papers and review articles were excluded. DATA EXTRACTION AND SYNTHESIS Two reviewers independently screened abstracts and full texts, assessed quality using Joanna Briggs Institute's critical appraisal tools, and extracted data from included studies. Thematic syntheses were used to identify prominent themes emerging from the data. RESULTS A total of 12 studies met the inclusion criteria, 11 of which were conducted in USA. These included six qualitative studies and six quantitative studies (including both survey and quasi-experimental designs). Three major themes with specific subthemes emerged: (1) Opportunity (uncertainty in the diagnosis and treatment of pulmonary nodules, willingness to participate in decision-making); (2) Ability (patient's lack of knowledge, physician's experience); and (3) Different worldview (misconception, distress among patients, preference for diagnosis and treatment). CONCLUSIONS Uncertainty in the management of pulmonary nodules is the opportunity to implement SDM. Patients' lack of knowledge, distress, and misunderstandings between healthcare providers and patients are both the main obstacles and the causes of the application of SDM.
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Affiliation(s)
- Jingmin Yuan
- Department of Preventive Medicine, Health Science Center, Yangtze University, Jingzhou, China
| | - Fenglin Xu
- Department of Nursing, Hubei College of Chinese Medicine, Jingzhou, China
| | - Yan Sun
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Hui Ren
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Department of Talent Highland, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Mingwei Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Sifang Feng
- Department of Pulmonary and Critical Care Medicine, Xi'an Jiaotong University Medical College First Affiliated Hospital, Xi'an, China
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Xue M, Li R, Liu J, Lu M, Li Z, Zhang H, Tian H. Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules. Front Oncol 2024; 14:1334504. [PMID: 39011482 PMCID: PMC11246902 DOI: 10.3389/fonc.2024.1334504] [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: 11/07/2023] [Accepted: 06/10/2024] [Indexed: 07/17/2024] Open
Abstract
Background This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs). Method We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA). Result The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively. Conclusion Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.
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Affiliation(s)
| | | | | | | | | | | | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
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Hsin-Hung C, En-Kuei T, Yun-Ju W, Fu-Zong W. Impact of annual trend volume of low-dose computed tomography for lung cancer screening on overdiagnosis, overmanagement, and gender disparities. Cancer Imaging 2024; 24:73. [PMID: 38867342 PMCID: PMC11170916 DOI: 10.1186/s40644-024-00716-5] [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: 03/21/2024] [Accepted: 06/05/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND With the increasing prevalence of nonsmoking-related lung cancer in Asia, Asian countries have increasingly adopted low-dose computed tomography (LDCT) for lung cancer screening, particularly in private screening programs. This study examined how annual LDCT volume affects lung cancer stage distribution, overdiagnosis, and gender disparities using a hospital-based lung cancer database. METHODS This study analyzed the annual utilized LDCT volume, clinical characteristics of lung cancer, stage shift distribution, and potential overdiagnosis. At the individual level, this study also investigated the relationship between stage 0 lung cancer (potential strict definition regarding overdiagnosis) and the clinical characteristics of lung cancer. RESULTS This study reviewed the annual trend of 4971 confirmed lung cancer cases from 2008 to 2021 and conducted a link analysis with an LDCT imaging examination database over these years. As the volume of lung cancer screenings has increased over the years, the number and proportion of stage 0 lung cancers have increased proportionally. Our study revealed that the incidence of stage 0 lung cancer increased with increasing LDCT scan volume, particularly during the peak growth period from 2017 to 2020. Conversely, stage 4 lung cancer cases remained consistent across different time intervals. Furthermore, the increase in the lung cancer screening volume had a more pronounced effect on the increase in stage 0 lung cancer cases among females than it had among males. The estimated potential for overdiagnosis brought about by the screening process, compared to non-participating individuals, ranged from an odds ratio of 7.617 to one of 17.114. Both strict and lenient definitions of overdiagnosis (evaluating cases of stage 0 lung cancer and stages 0 to 1 lung cancer) were employed. CONCLUSIONS These results provide population-level evidence of potential lung cancer overdiagnosis in the Taiwanese population due to the growing use of LDCT screening, particularly concerning the strict definition of stage 0 lung cancer. The impact was greater in the female population than in the male population, especially among females younger than 40 years. To improve lung cancer screening in Asian populations, creating risk-based prediction models for smokers and nonsmokers, along with gender-specific strategies, is vital for ensuring survival benefits and minimizing overdiagnosis.
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Affiliation(s)
- Chen Hsin-Hung
- Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, 813414, Taiwan
| | - Tang En-Kuei
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, 813414, Taiwan
| | - Wu Yun-Ju
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Wu Fu-Zong
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Mao Y, Cai J, Heuvelmans MA, Vliegenthart R, Groen HJM, Oudkerk M, Vonder M, Dorrius MD, de Bock GH. Performance of Lung-RADS in different target populations: a systematic review and meta-analysis. Eur Radiol 2024; 34:1877-1892. [PMID: 37646809 PMCID: PMC10873443 DOI: 10.1007/s00330-023-10049-9] [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: 01/06/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVES Multiple lung cancer screening studies reported the performance of Lung CT Screening Reporting and Data System (Lung-RADS), but none systematically evaluated its performance across different populations. This systematic review and meta-analysis aimed to evaluate the performance of Lung-RADS (versions 1.0 and 1.1) for detecting lung cancer in different populations. METHODS We performed literature searches in PubMed, Web of Science, Cochrane Library, and Embase databases on October 21, 2022, for studies that evaluated the accuracy of Lung-RADS in lung cancer screening. A bivariate random-effects model was used to estimate pooled sensitivity and specificity, and heterogeneity was explored in stratified and meta-regression analyses. RESULTS A total of 31 studies with 104,224 participants were included. For version 1.0 (27 studies, 95,413 individuals), pooled sensitivity was 0.96 (95% confidence interval [CI]: 0.90-0.99) and pooled specificity was 0.90 (95% CI: 0.87-0.92). Studies in high-risk populations showed higher sensitivity (0.98 [95% CI: 0.92-0.99] vs. 0.84 [95% CI: 0.50-0.96]) and lower specificity (0.87 [95% CI: 0.85-0.88] vs. 0.95 (95% CI: 0.92-0.97]) than studies in general populations. Non-Asian studies tended toward higher sensitivity (0.97 [95% CI: 0.91-0.99] vs. 0.91 [95% CI: 0.67-0.98]) and lower specificity (0.88 [95% CI: 0.85-0.90] vs. 0.93 [95% CI: 0.88-0.96]) than Asian studies. For version 1.1 (4 studies, 8811 individuals), pooled sensitivity was 0.91 (95% CI: 0.83-0.96) and specificity was 0.81 (95% CI: 0.67-0.90). CONCLUSION Among studies using Lung-RADS version 1.0, considerable heterogeneity in sensitivity and specificity was noted, explained by population type (high risk vs. general), population area (Asia vs. non-Asia), and cancer prevalence. CLINICAL RELEVANCE STATEMENT Meta-regression of lung cancer screening studies using Lung-RADS version 1.0 showed considerable heterogeneity in sensitivity and specificity, explained by the different target populations, including high-risk versus general populations, Asian versus non-Asian populations, and populations with different lung cancer prevalence. KEY POINTS • High-risk population studies showed higher sensitivity and lower specificity compared with studies performed in general populations by using Lung-RADS version 1.0. • In non-Asian studies, the diagnostic performance of Lung-RADS version 1.0 tended to be better than in Asian studies. • There are limited studies on the performance of Lung-RADS version 1.1, and evidence is lacking for Asian populations.
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Affiliation(s)
- Yifei Mao
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Jiali Cai
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Marjolein A Heuvelmans
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Harry J M Groen
- Department of Pulmonary Diseases, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Matthijs Oudkerk
- Institute for Diagnostic Accuracy, Prof. Wiersmastraat 5, 9713 GH, Groningen, the Netherlands
| | - Marleen Vonder
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Monique D Dorrius
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
- Department of Radiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Geertruida H de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands.
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Wu FZ, Wu YJ, Chen CS, Tang EK. Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features. Acad Radiol 2023; 30:2856-2869. [PMID: 37080884 DOI: 10.1016/j.acra.2023.02.033] [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: 11/15/2022] [Revised: 12/23/2022] [Accepted: 02/27/2023] [Indexed: 04/22/2023]
Abstract
RATIONALES AND OBJECTIVES To investigate the prognostic value of the radiomic-based prediction model in predicting the interval growth rate of persistent subsolid nodules (SSNs) with an initial size of ≤ 3 cm manifesting as lung adenocarcinomas. MATERIALS AND METHODS A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent a series of CT examinations at our hospital between 2012 and 2022 were included in this study. Forty-one radiomic features were extracted from each volumetric region of interest. Radiomic features combined with conventional clinical and semantic parameters were then selected for radiomic-based model building. To investigate the model performance in terms of substantial SSN growth and stage shift growth, the model performance was compared by the area under the curve (AUC) obtained by receiver operating characteristic analysis. RESULTS The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based model (Model 2) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.869 (95% CI: 0.799-0.922). In comparison with Model 1 (clinical characteristics and CT semantic features), Model 2 performed better than Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p = 0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic features was built for substantial SSN growth prediction. For the stage shift growth, a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815-0.933). Compared with Model 3 (clinical characteristics and CT semantic features), Model 4 performed better than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883, p = 0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification, follow-up period, and three radiomic features was built to predict the stage shift growth. CONCLUSION Radiomic-based models have superior utility in estimating the prognostic interval growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic models in terms of substantial interval growth and stage shift growth, potentially guiding clinical decision-making with follow-up strategies of SSNs in personalized precision medicine.
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Affiliation(s)
- Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, 70, Lien-hai Road, Kaohsiung 80424, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
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Lam DCL, Liam CK, Andarini S, Park S, Tan DSW, Singh N, Jang SH, Vardhanabhuti V, Ramos AB, Nakayama T, Nhung NV, Ashizawa K, Chang YC, Tscheikuna J, Van CC, Chan WY, Lai YH, Yang PC. Lung Cancer Screening in Asia: An Expert Consensus Report. J Thorac Oncol 2023; 18:1303-1322. [PMID: 37390982 DOI: 10.1016/j.jtho.2023.06.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 05/23/2023] [Accepted: 06/10/2023] [Indexed: 07/02/2023]
Abstract
INTRODUCTION The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West. METHOD A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population. RESULTS Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment. CONCLUSIONS Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.
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Affiliation(s)
- David Chi-Leung Lam
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Chong-Kin Liam
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Sita Andarini
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia - Persahabatan Hospital, Jakarta, Indonesia
| | - Samina Park
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Daniel S W Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore; Division of Medical Oncology, National Cancer Centre Singapore, Duke-NUS Medical School, Singapore
| | - Navneet Singh
- Lung Cancer Clinic, Department of Pulmonary Medicine, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Seung Hun Jang
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Anyang, Korea
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, People's Republic of China
| | - Antonio B Ramos
- Department of Thoracic Surgery and Anesthesia, Lung Center of the Philippines, Quezon City, Philippines
| | - Tomio Nakayama
- Division of Screening Assessment and Management, National Cancer Center Institute for Cancer Control, Japan
| | - Nguyen Viet Nhung
- Vietnam National Lung Hospital, University of Medicine and Pharmacy, VNU Hanoi, Vietnam
| | - Kazuto Ashizawa
- Department of Clinical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jamsak Tscheikuna
- Division of Respiratory Disease and Tuberculosis, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | | | - Wai Yee Chan
- Imaging Department, Gleneagles Hospital Kuala Lumpur, Jalan Ampang, 50450 Kuala Lumpur; Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia
| | - Yeur-Hur Lai
- School of Nursing, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Nursing, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Pan-Chyr Yang
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan & National Taiwan University Hospital, Taipei, Taiwan.
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Liu YC, Liang CH, Wu YJ, Chen CS, Tang EK, Wu FZ. Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns. Diagnostics (Basel) 2023; 13:2674. [PMID: 37627933 PMCID: PMC10453827 DOI: 10.3390/diagnostics13162674] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
With the popularization of lung cancer screening, many persistent subsolid nodules (SSNs) have been identified clinically, especially in Asian non-smokers. However, many studies have found that SSNs exhibit heterogeneous growth trends during long-term follow ups. This article adopted a narrative approach to extensively review the available literature on the topic to explore the definitions, rationale, and clinical application of different interval growths of subsolid pulmonary nodule management and follow-up strategies. The development of SSN growth thresholds with different growth patterns could support clinical decision making with follow-up guidelines to reduce over- and delayed diagnoses. In conclusion, using different SSN growth thresholds could optimize the follow-up management and clinical decision making of SSNs in lung cancer screening programs. This could further reduce the lung cancer mortality rate and potential harm from overdiagnosis and over management.
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Affiliation(s)
- Yung-Chi Liu
- Department of Radiology, Xiamen Chang Gung Hospital, Xiamen 361028, China;
- Department of Imaging Technology Division, Xiamen Chang Gung Hospital, Xiamen 361028, China
- Department of Healthcare Administration Department, Xiamen Chang Gung Hospital, Xiamen 361028, China
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 112304, Taiwan;
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Education, National Sun Yat-Sen University, Kaohsiung 804241, Taiwan
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Triphuridet N, Zhang SS, Nagasaka M, Gao Y, Zhao JJ, Syn NL, Hanaoka T, Ou SHI, Shum E. Low-Dose Computed Tomography (LDCT) Lung Cancer Screening in Asian Female Never-Smokers Is as Efficacious in Detecting Lung Cancer as in Asian Male Ever-Smokers: A Systematic Review and Meta-Analysis. J Thorac Oncol 2023; 18:698-717. [PMID: 36775191 DOI: 10.1016/j.jtho.2023.01.094] [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: 12/11/2022] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/12/2023]
Abstract
INTRODUCTION Lung cancer in never-smokers is the major cancer cause of death globally. We compared the efficacy of low-dose computed tomography (LDCT) lung cancer screening among never-smokers versus ever-smokers using systematic review and meta-analysis. METHODS LDCT lung cancer screening studies that simultaneously included both ever-smoker and never-smoker participants published by April 30, 2021, were searched through PubMed and Scopus. Primary outcome measure was relative risk (RR) of lung cancer diagnosed among never-smokers versus ever-smokers. RESULTS A total of 14 studies (13 from Asia) were included (141,396 ever-smokers, 109,251 never-smokers, 1961 lung cancer cases diagnosed). RR of lung cancer diagnosed between ever-smokers versus never-smokers overall was 1.21 (95% confidence interval [CI]: 0.89-1.65), 1.37 (95% CI: 1.08-1.75) among males, and 0.88 (95% CI: 0.59-1.31) among females. RR was 1.78 (95% CI: 1.41-2.24) and 1.22 (95% CI: 0.89-1.68) for Asian female never-smokers versus male never-smokers and versus male ever-smokers, respectively, and 0.99 (95% CI: 0.65-1.50) versus high-risk ever-smokers (≥30 pack-years). Proportional meta-analysis revealed significantly more lung cancers diagnosed at first scan (95.4% [95% CI: 84.9-100.0] versus 70.9% [95% CI: 54.6-84.9], p = 0.010) and at stage 1 (88.5% [95% CI: 79.3-95.4] versus 79.7% [95% CI: 71.1-87.4], p = 0.071) among never-smokers versus ever-smokers, respectively. RR of lung cancer death and 5-year all-cause mortality in never-smokers versus ever-smokers was 0.27 (95% CI: 0.1-0.55, p < 0.001) and 0.13 (95% CI: 0.05-0.33, p < 0.001), respectively. CONCLUSIONS The RR of lung cancer detected by LDCT screening among female never-smokers and male ever-smokers in Asia was statistically similar. Overall and lung cancer specific mortality from the lung cancer diagnosed from LDCT screening was significantly reduced among never-smokers compared to ever-smokers.
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Affiliation(s)
- Natthaya Triphuridet
- Department of Medicine, Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Shannon S Zhang
- Department of Medicine, Division of Hematology-Oncology, University of California Irvine School of Medicine, Orange, California
| | - Misako Nagasaka
- Department of Medicine, Division of Hematology-Oncology, University of California Irvine School of Medicine, Orange, California; Chao Family Comprehensive Cancer Center, Orange, California; Dalian Best Biotechnology Ltd., Beijing, People's Republic of China
| | - Yanfei Gao
- Division of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki, Kanagawa, Japan
| | - Joseph J Zhao
- National University of Singapore Yong Loo Lin School of Medicine, Singapore
| | - Nicholas L Syn
- National University of Singapore Yong Loo Lin School of Medicine, Singapore
| | - Takaomi Hanaoka
- Department of Thoracic Surgery, JA Nagano North Alps Medical Center Azumi Hospital, Nagano, Japan
| | - Sai-Hong Ignatius Ou
- Department of Medicine, Division of Hematology-Oncology, University of California Irvine School of Medicine, Orange, California; Chao Family Comprehensive Cancer Center, Orange, California.
| | - Elaine Shum
- Division of Medical Oncology, Department of Medicine, NYU Perlmutter Cancer Center/NYU Langone Health, New York, New York
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Guo L, Yu Y, Yang F, Gao W, Wang Y, Xiao Y, Du J, Tian J, Yang H. Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis. Chin Med J (Engl) 2023; 136:1047-1056. [PMID: 37101352 PMCID: PMC10228483 DOI: 10.1097/cm9.0000000000002353] [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: 06/09/2022] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer. METHODS MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test. RESULTS A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies. CONCLUSIONS Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.
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Affiliation(s)
- Lanwei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan 450008, China
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Yue Yu
- Clinical Trials Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Funa Yang
- Department of Nursing, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan 450008, China
| | - Wendong Gao
- Henan University of Chinese Medicine, Zhengzhou, Henan 450046, China
| | - Yu Wang
- Nursing and Health School of Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Yao Xiao
- Nursing and Health School of Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Jia Du
- International College of Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, Gansu 730000, China
| | - Haiyan Yang
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, China
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10
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Ke X, Hu W, Su X, Huang F, Lai Q. Potential of artificial intelligence based on chest computed tomography to predict the nature of part-solid nodules. THE CLINICAL RESPIRATORY JOURNAL 2023; 17:320-328. [PMID: 36740215 PMCID: PMC10113279 DOI: 10.1111/crj.13597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 01/05/2023] [Accepted: 01/30/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND The potential of artificial intelligence (AI) to predict the nature of part-solid nodules based on chest computed tomography (CT) is still under exploration. OBJECTIVE To determine the potential of AI to predict the nature of part-solid nodules. METHODS Two hundred twenty-three patients diagnosed with part-solid nodules (241) by chest CT were retrospectively collected that were divided into benign group (104) and malignant group (137). Intraclass correlation coefficient (ICC) was used to assess the agreement in predicting malignancy, and the predictive effectiveness was compared between AI and senior radiologists. The parameters measured by AI and the size of solid components measured by senior radiologists were compared between two groups. Receiver operating characteristic (ROC) curve was chosen for calculating the Youden index of each quantitative parameter, which has statistical significance between two groups. Binary logistic regression performed on the significant indicators to suggest predictors of malignancy. RESULTS AI was in moderate agreement with senior radiologists (ICC = 0.686). The sensitivity, specificity and accuracy of two groups were close (p > 0.05). The longest diameter, volume and mean CT attenuation value and the largest diameter of solid components between benign and malignant groups were different significantly (p < 0.001). Logistic regression analysis showed that the longest diameter and mean CT attenuation value and the largest diameter of solid components were indicators for malignant part-solid nodules, the threshold of which were 9.45 mm, 425.0 HU and 3.45 mm, respectively. CONCLUSION Potential of quantitative parameter measured by AI to predict malignant part-solid nodules can provide a certain value for the clinical management.
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Affiliation(s)
- Xiaoting Ke
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Weiyi Hu
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Xianyan Su
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Fang Huang
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Qingquan Lai
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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11
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Toward More Effective Lung Cancer Risk Stratification to Empower Screening Programs for the Asian Nonsmoking Population. J Am Coll Radiol 2023; 20:156-161. [PMID: 36646597 DOI: 10.1016/j.jacr.2022.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/14/2022] [Accepted: 10/14/2022] [Indexed: 01/15/2023]
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12
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Zhang Z, Zhou L, Yang F, Li X. The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management. Front Oncol 2022; 12:1011712. [PMID: 36568242 PMCID: PMC9772280 DOI: 10.3389/fonc.2022.1011712] [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: 08/04/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
The high detection rate of pulmonary subsolid nodules (SSN) is an increasingly crucial clinical issue due to the increased number of screening tests and the growing popularity of low-dose computed tomography (LDCT). The persistence of SSN strongly suggests the possibility of malignancy. Guidelines have been published over the past few years and guide the optimal management of SSNs, but many remain controversial and confusing for clinicians. Therefore, in-depth research on the natural growth history of persistent pulmonary SSN can help provide evidence-based medical recommendations for nodule management. In this review, we briefly describe the differential diagnosis, growth patterns and rates, genetic characteristics, and factors that influence the growth of persistent SSN. With the advancement of radiomics and artificial intelligence (AI) technology, individualized evaluation of SSN becomes possible. These technologies together with liquid biopsy, will promote the transformation of current diagnosis and follow-up strategies and provide significant progress in the precise management of subsolid nodules in the early stage of lung cancer.
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13
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Zou K, Sun P, Huang H, Zhuo H, Qie R, Xie Y, Luo J, Li N, Li J, He J, Aschebrook-Kilfoy B, Zhang Y. Etiology of lung cancer: Evidence from epidemiologic studies. JOURNAL OF THE NATIONAL CANCER CENTER 2022; 2:216-225. [PMID: 39036545 PMCID: PMC11256564 DOI: 10.1016/j.jncc.2022.09.004] [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: 01/25/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 12/05/2022] Open
Abstract
Lung cancer is one of the leading causes of cancer incidence and mortality worldwide. While smoking, radon, air pollution, as well as occupational exposure to asbestos, diesel fumes, arsenic, beryllium, cadmium, chromium, nickel, and silica are well-established risk factors, many lung cancer cases cannot be explained by these known risk factors. Over the last two decades the incidence of adenocarcinoma has risen, and it now surpasses squamous cell carcinoma as the most common histologic subtype. This increase warrants new efforts to identify additional risk factors for specific lung cancer subtypes as well as a comprehensive review of current evidence from epidemiologic studies to inform future studies. Given the myriad exposures individuals experience in real-world settings, it is essential to investigate mixture effects from complex exposures and gene-environment interactions in relation to lung cancer and its subtypes.
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Affiliation(s)
- Kaiyong Zou
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peiyuan Sun
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huang Huang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haoran Zhuo
- Yale School of Public Health, New Haven, United States of America
| | - Ranran Qie
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuting Xie
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiajun Luo
- Department of Public Health Sciences, the University of Chicago, Chicago, United States of America
| | - Ni Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | | | - Yawei Zhang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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14
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Wu YJ, Wu FZ, Yang SC, Tang EK, Liang CH. Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education. Diagnostics (Basel) 2022; 12:diagnostics12051064. [PMID: 35626220 PMCID: PMC9139351 DOI: 10.3390/diagnostics12051064] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/14/2022] [Accepted: 04/22/2022] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.
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Affiliation(s)
- Yun-Ju Wu
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan;
| | - Fu-Zong Wu
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Correspondence:
| | - Shu-Ching Yang
- Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan;
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15
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Song X, Zhao Q, Zhang H, Xue W, Xin Z, Xie J, Zhang X. Development and Validation of a Preoperative CT-Based Nomogram to Differentiate Invasive from Non-Invasive Pulmonary Adenocarcinoma in Solitary Pulmonary Nodules. Cancer Manag Res 2022; 14:1195-1208. [PMID: 35342306 PMCID: PMC8948523 DOI: 10.2147/cmar.s357385] [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: 01/20/2022] [Accepted: 03/08/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Patients and Methods Results Conclusion
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Affiliation(s)
- Xin Song
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
- North China University of Science and Technology, Tangshan, People’s Republic of China
| | - Qingtao Zhao
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Hua Zhang
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Wenfei Xue
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Zhifei Xin
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
| | - Jianhua Xie
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
- North China University of Science and Technology, Tangshan, People’s Republic of China
| | - Xiaopeng Zhang
- Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, People’s Republic of China
- Correspondence: Xiaopeng Zhang, Hebei General Hospital, No. 348, Heping Western Road, Xinhua District, Shijiazhuang, 050000, People’s Republic of China, Tel +8613722865878, Email
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16
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Wu FZ, Wu YJ, Chen CS, Yang SC. Impact of Smoking Status on Lung Cancer Characteristics and Mortality Rates between Screened and Non-Screened Lung Cancer Cohorts: Real-World Knowledge Translation and Education. J Pers Med 2022; 12:jpm12010026. [PMID: 35055341 PMCID: PMC8780024 DOI: 10.3390/jpm12010026] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/13/2021] [Accepted: 11/15/2021] [Indexed: 12/14/2022] Open
Abstract
This was a retrospective hospital-based cohort study of participants diagnosed with lung cancer in the lung cancer register database, and our goal was to evaluate the impact of smoking and screening status on lung cancer characteristics and clinical outcomes. According to the hospital-based lung cancer register database, a total of 2883 lung cancers were diagnosed in 2883 patients between January 2007 and September 2017, which were divided into four groups according to smoking and screening status. A comparison was performed in terms of clinical characteristics and outcomes of lung cancer between the four groups. For non-smokers, age, gender, screened status, tumor size, targeted therapy, and curative surgery were independent prognostic factors of overall survival for lung cancer subjects. However, screened status and gender were not significant prognostic factors for lung cancer survival in smokers with lung cancer. For the non-smoker group, about 4.9% of lung cancer subjects (N = 81) were detected by screening. However, only 0.97% of lung cancer subjects (N = 12) were detected by screening in smokers. This could be attributed to smokers' negative attitudes and low socioeconomic status preventing LDCT lung cancer screening. In summary, our real-world data suggest that effectively encouraging smokers to be more willing to participate in lung cancer screening programs with screening allowance and educational training in the future is an important issue.
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Affiliation(s)
- Fu-Zong Wu
- Institute of Education, National Sun Yat-sen University, 70, Lien-Hai Road, Kaohsiung 80424, Taiwan;
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- Department of Medical Research and Education, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
| | - Shu-Ching Yang
- Institute of Education, National Sun Yat-sen University, 70, Lien-Hai Road, Kaohsiung 80424, Taiwan;
- Correspondence:
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17
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Li Y, Du Y, Huang Y, Zhao Y, Sidorenkov G, Vonder M, Cui X, Fan S, Dorrius MD, Vliegenthart R, Groen HJM, Liu S, Song F, Chen K, de Bock GH, Ye Z. Community-based lung cancer screening by low-dose computed tomography in China: First round results and a meta-analysis. Eur J Radiol 2021; 144:109988. [PMID: 34695695 DOI: 10.1016/j.ejrad.2021.109988] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To evaluate the efficiency of low-dose computed tomography (LDCT) screening for lung cancer in China by analyzing the baseline results of a community-based screening study accompanied with a meta-analysis. METHODS A first round of community-based lung cancer screening with LDCT was conducted in Tianjin, China, and a systematic literature search was performed to identify LDCT screening and registry-based clinical studies for lung cancer in China. Baseline results in the community-based screening study were described by participant risk level and the lung cancer detection rate was compared with the pooled rate among the screening studies. The percentage of patients per stage was compared between the community-based study and screening and clinical studies. RESULTS In the community-based study, 5523 participants (43.6% men) underwent LDCT. The lung cancer detection rate was 0.5% (high-risk, 1.2%; low-risk, 0.4%), with stage I disease present in 70.0% (high-risk, 50.0%; low-risk, 83.3%), and the adenocarcinoma present in 84.4% (high-risk, 61.5%; low-risk, 100%). Among all screen-detected lung cancer, women accounted for 8.3% and 66.7% in the high- and low-risk group, respectively. In the screening studies from mainland China, the lung cancer detection rate 0.6% (95 %CI: 0.3%-0.9%) for high-risk populations. The proportions with carcinoma in situ and stage I disease in the screening and clinical studies were 76.4% (95 %CI: 66.3%-85.3%) and 15.2% (95 %CI: 11.8%-18.9%), respectively. CONCLUSIONS The stage shift of lung cancer due to screening suggests a potential effectiveness of LDCT screening in China. Nearly 70% of screen-detected lung cancers in low-risk populations are identified in women.
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Affiliation(s)
- Yanju Li
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Radiology, Tianjin, People's Republic of China
| | - Yihui Du
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands
| | - Yubei Huang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Cancer Epidemiology and Biostatistics, Tianjin, People's Republic of China
| | - Yingru Zhao
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Radiology, Tianjin, People's Republic of China
| | - Grigory Sidorenkov
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands
| | - Marleen Vonder
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands
| | - Xiaonan Cui
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Radiology, Tianjin, People's Republic of China
| | - Shuxuan Fan
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Radiology, Tianjin, People's Republic of China
| | - Monique D Dorrius
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands
| | - Rozemarijn Vliegenthart
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands
| | - Harry J M Groen
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, the Netherlands
| | - Shiyuan Liu
- Shanghai Changzheng Hospital, The Second Military Medical University Shanghai, Department of Radiology, Shanghai, People's Republic of China
| | - Fengju Song
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Cancer Epidemiology and Biostatistics, Tianjin, People's Republic of China
| | - Kexin Chen
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Cancer Epidemiology and Biostatistics, Tianjin, People's Republic of China.
| | - Geertruida H de Bock
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
| | - Zhaoxiang Ye
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Department of Radiology, Tianjin, People's Republic of China.
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Hung YC, Tang EK, Wu YJ, Chang CJ, Wu FZ. Impact of low-dose computed tomography for lung cancer screening on lung cancer surgical volume: The urgent need in health workforce education and training. Medicine (Baltimore) 2021; 100:e26901. [PMID: 34397918 PMCID: PMC8360459 DOI: 10.1097/md.0000000000026901] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 07/26/2021] [Indexed: 01/04/2023] Open
Abstract
This study aimed to investigate the time trend variation in the surgical volume and prognostic outcome of patients with lung cancer after the gradual prolonged implementation of a low-dose computed tomography (LDCT) lung cancer screening program.Using the hospital-based cancer registry data on number of patients with lung cancer and deaths from 2008 to 2017, we conducted a retrospective study using a hospital-based cohort to investigate the relationship between changes in lung cancer surgical volume, the proportion of lung-sparing surgery, and prolonged prognostic outcomes after the gradual implementation of the LDCT lung cancer screening program in recent years.From 2008 to 2017, 3251 patients were diagnosed with lung cancer according to the hospital-based cancer registry. The 5-year mortality rate decreased gradually from 83.54% to 69.44% between 2008 and 2017. The volume of total lung cancer surgical procedures and proportion of lung-sparing surgery performed gradually increased significantly from 2008 to 2017, especially from 2014 to 2017 after implementation of a large volume of LDCT lung cancer screening examinations. In conclusion, our real-world data suggest that there will be an increase in cases of operable early-stage lung cancers, which in turn will increase the surgical volume and proportion of lung-sparing surgery, after the gradual implementation of the LDCT lung cancer screening program in recent years. These findings suggest the importance of a successful national policy regarding LDCT screening programs, regulation of shortage of thoracic surgeons, thoracic radiologist workforce training positions, and education programs.
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Affiliation(s)
- Yi-Chi Hung
- Laboratory of Tissue-Engineering, Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, Taiwan
- Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan
- Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Yun-Ju Wu
- Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chen-Jung Chang
- Laboratory of Tissue-Engineering, Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, Taiwan
| | - Fu-Zong Wu
- Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan
- Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Medicine, School of Medicine, Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
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A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules. Sci Rep 2021; 11:66. [PMID: 33462251 PMCID: PMC7814025 DOI: 10.1038/s41598-020-79690-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 12/08/2020] [Indexed: 12/21/2022] Open
Abstract
This study aims to predict the histological invasiveness of pulmonary adenocarcinoma spectrum manifesting with subsolid nodules ≦ 3 cm using the preoperative CT-based radiomic approach. A total of 186 patients with 203 SSNs confirmed with surgically pathologic proof were retrospectively reviewed from February 2016 to March 2020 for training cohort modeling. The validation cohort included 50 subjects with 57 SSNs confirmed with surgically pathologic proof from April 2020 to August 2020. CT-based radiomic features were extracted using an open-source software with 3D nodular volume segmentation manually. The association between CT-based conventional features/selected radiomic features and histological invasiveness of pulmonary adenocarcinoma status were analyzed. Diagnostic models were built using conventional CT features, selected radiomic CT features and experienced radiologists. In addition, we compared diagnostic performance between radiomic CT feature, conventional CT features and experienced radiologists. In the training cohort of 203 SSNs, there were 106 invasive lesions and 97 pre-invasive lesions. Logistic analysis identified that a selected radiomic feature named GLCM_Entropy_log10 was the predictor for histological invasiveness of pulmonary adenocarcinoma spectrum (OR: 38.081, 95% CI 2.735–530.309, p = 0.007). The sensitivity and specificity for predicting histological invasiveness of pulmonary adenocarcinoma spectrum using the cutoff value of CT-based radiomic parameter (GLCM_Entropy_log10) were 84.8% and 79.2% respectively (area under curve, 0.878). The diagnostic model of CT-based radiomic feature was compared to those of conventional CT feature (morphologic and quantitative) and three experienced radiologists. The diagnostic performance of radiomic feature was similar to those of the quantitative CT feature (nodular size and solid component, both lung and mediastinal window) in prediction invasive pulmonary adenocarcinoma (IPA). The AUC value of CT radiomic feature was higher than those of conventional CT morphologic feature and three experienced radiologists. The c-statistic of the training cohort model was 0.878 (95% CI 0.831–0.925) and 0.923 (0.854–0.991) in the validation cohort. Calibration was good in both cohorts. The diagnostic performance of CT-based radiomic feature is not inferior to solid component (lung and mediastinal window) and nodular size for predicting invasiveness. CT-based radiomic feature and nomogram could help to differentiate IPA lesions from preinvasive lesions in the both independent training and validation cohorts. The nomogram may help clinicians with decision making in the management of subsolid nodules.
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Lin DQ, Zhu JG, Xu XH, Xiao K, Wen XQ, Zheng QF, Zhou YH, Cai XY. Chronic Progression of Lung Cancer Recurrence After Surgery: Warning Role of Postoperative Pneumonia. Cancer Manag Res 2021; 13:7387-7398. [PMID: 34602824 PMCID: PMC8481098 DOI: 10.2147/cmar.s327646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/11/2021] [Indexed: 02/05/2023] Open
Abstract
PURPOSE The association between the process of postoperative pneumonia and lung cancer recurrence remains elusive in lung cancer surgery. Herein, the association between postoperative pneumonia and lung cancer recurrence was investigated, emphasizing the warning role of postoperative specific pneumonia in primary lung cancer resection patients. METHODS The occurrence of postoperative pneumonia was assessed in 4-6 months (PPFS), 7-12 months (PPST), and lung cancer recurrence within 1 year (LRO) in 332 patients. The primary outcome was the development of PPST and LRO according to PPFS occurrence. The relevant risk factors of PPFS, PPST, and LRO were identified through multivariable regression analysis. RESULTS During follow-up, 151 (45.48%) participants experienced PPFS. Irrespective of the existing postoperative pneumonia in 1-3 months (PPOT), PPFS significantly increased the risk of PPST (P < 0.01) and LRO (P < 0.01), and persistent PPST further increased the risk of LRO (P < 0.001). The generalized estimating equation identified chemotherapy as an independent risk factor for PPFS and PPST. CONCLUSION PPFS was associated with the increased risk of PPST and LRO. Postoperative pulmonary inflammation assessed 4 months post-surgery also significantly influenced LRO development, indicating a need for close follow-up of lung inflammatory conditions to improve patient outcomes.
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Affiliation(s)
- Dong-qi Lin
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, People’s Republic of China
| | - Jin-guo Zhu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, People’s Republic of China
| | - Xiao-hua Xu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, People’s Republic of China
| | - Ke Xiao
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, People’s Republic of China
| | - Xu-qing Wen
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, People’s Republic of China
| | - Qi-fa Zheng
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, People’s Republic of China
| | - Yu-hua Zhou
- Nursing Department, Shantou Central Hospital, Shantou, Guangdong, People’s Republic of China
| | - Xin-ying Cai
- Clinical Research Center, Shantou Central Hospital, Shantou, Guangdong, People’s Republic of China
- Correspondence: Xin-ying Cai Clinical Research Center, Shantou Central Hospital, Wai-ma Road 114, Shantou, Guangdong, People’s Republic of ChinaTel +86 754-88903584Fax +86 754-88548117 Email
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Chen Z, Jiang S, Li Z, Rao L, Zhang X. Clinical Value of 18F-FDG PET/CT in Prediction of Visceral Pleural Invasion of Subsolid Nodule Stage I Lung Adenocarcinoma. Acad Radiol 2020; 27:1691-1699. [PMID: 32063495 DOI: 10.1016/j.acra.2020.01.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/16/2020] [Accepted: 01/16/2020] [Indexed: 12/11/2022]
Abstract
RATIONALE AND OBJECTIVES This study investigated the utility of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting visceral pleural invasion (VPI) of subsolid nodule (SSN) stage I lung adenocarcinoma. MATERIALS AND METHODS A retrospective analysis of 18F-FDG PET/CT data from 65 postsurgical cases with surgical pathology-confirmed SSN lung adenocarcinoma identified significant VPI predictors using multivariate logistic regression. RESULTS Nodule and solid component sizes, solid component-to-tumor ratios, pleural indentations, distances between nodules and pleura, and maximum standardized uptake values (SUVmax) differed significantly between VPI-positive (n = 30) and VPI-negative (n = 35) cases on univariate analysis. The distance between the nodule and pleura and SUVmax were significant independent VPI predictors on multivariate analysis. Areas under the curve of the distance between the nodule and pleura and SUVmax on receiver operating characteristic curves were 0.76 and 0.79, respectively; both factors were 0.90. The area under the curve of combined predictors was significantly superior to the distance between the nodule and pleura only but not SUVmax alone. The threshold of the distance between the nodule and pleura, to predict VPI was 4.50 mm, with 96.67% sensitivity, and 57.14% specificity. The threshold of SUVmax to predict VPI was 1.05, with 100% sensitivity and 60% specificity. The sensitivity and specificity of model 2 using the independent predictive factors were 96.67%, and 71.43%, respectively. CONCLUSION Distance between the nodule and pleura and SUVmax are independent predictors of VPI in SSN stage I lung adenocarcinoma. Further, combining these factors improves their predictive ability.
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Affiliation(s)
- Zhifeng Chen
- Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-Sen University, 58#, Zhongshan 2 Road, Guangzhou 510080, China
| | - Suxiang Jiang
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Zhoulei Li
- Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-Sen University, 58#, Zhongshan 2 Road, Guangzhou 510080, China
| | - Liangjun Rao
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiangsong Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-Sen University, 58#, Zhongshan 2 Road, Guangzhou 510080, China.
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Lu H, Kim J, Qi J, Li Q, Liu Y, Schabath MB, Ye Z, Gillies RJ, Balagurunathan Y. Multi-Window CT Based Radiological Traits for Improving Early Detection in Lung Cancer Screening. Cancer Manag Res 2020; 12:12225-12238. [PMID: 33273859 PMCID: PMC7707434 DOI: 10.2147/cmar.s246609] [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: 01/19/2020] [Accepted: 10/03/2020] [Indexed: 11/23/2022] Open
Abstract
Rationale and Objectives Evaluate ability of radiological semantic traits assessed on multi-window computed tomography (CT) to predict lung cancer risk. Materials and Methods A total of 199 participants were investigated, including 60 incident lung cancers and 139 benign positive controls. Twenty lung window features and 2 mediastinal window features were extracted and scored on a point scale in three screening rounds. Multivariate logistic regression analysis was used to explore the association of these radiological traits with the risk of developing lung cancer. The areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value (PPV) were computed to evaluate the best predictive model. Results Combining mediastinal window-specific features with the lung window features-based model significantly improves performance compared to individual window features. Model performance is consistent both at baseline and the first follow-up scan, with an AUROC increased from 0.822 to 0.871 (p = 0.009) and from 0.877 to 0.917 (p = 0.008), respectively, for single to multi-window feature models. We also find that the multi-window CT based model showed better specificity and PPV, with PPV at the second follow-up scan improved to 0.953. Conclusion We find combining window semantic features improves model performance in identifying cancerous nodules. We also find that lung window features are more informative compared to mediastinal features in predicting malignancy.
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Affiliation(s)
- Hong Lu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China.,Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jongphil Kim
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jin Qi
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China.,Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Qian Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China
| | - Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Yoganand Balagurunathan
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.,Department of Machine Language, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Artificial neural networks improve LDCT lung cancer screening: a comparative validation study. BMC Cancer 2020; 20:1023. [PMID: 33092589 PMCID: PMC7579928 DOI: 10.1186/s12885-020-07465-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 09/28/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND This study proposes a prediction model for the automatic assessment of lung cancer risk based on an artificial neural network (ANN) with a data-driven approach to the low-dose computed tomography (LDCT) standardized structure report. METHODS This comparative validation study analysed a prospective cohort from Chiayi Chang Gung Memorial Hospital, Taiwan. In total, 836 asymptomatic patients who had undergone LDCT scans between February 2017 and August 2018 were included, comprising 27 lung cancer cases and 809 controls. A derivation cohort of 602 participants (19 lung cancer cases and 583 controls) was collected to construct the ANN prediction model. A comparative validation of the ANN and Lung-RADS was conducted with a prospective cohort of 234 participants (8 lung cancer cases and 226 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models. RESULTS At the cut-off of category 3, the Lung-RADS had a sensitivity of 12.5%, specificity of 96.0%, positive predictive value of 10.0%, and negative predictive value of 96.9%. At its optimal cut-off value, the ANN had a sensitivity of 75.0%, specificity of 85.0%, positive predictive value of 15.0%, and negative predictive value of 99.0%. The area under the ROC curve was 0.764 for the Lung-RADS and 0.873 for the ANN (P = 0.01). The two most important predictors used by the ANN for predicting lung cancer were the documented sizes of partially solid nodules and ground-glass nodules. CONCLUSIONS Compared to the Lung-RADS, the ANN provided better sensitivity for the detection of lung cancer in an Asian population. In addition, the ANN provided a more refined discriminative ability than the Lung-RADS for lung cancer risk stratification with population-specific demographic characteristics. When lung nodules are detected and documented in a standardized structured report, ANNs may better provide important insights for lung cancer prediction than conventional rule-based criteria.
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Association of cancer screening and residing in a coal-polluted East Asian region with overall survival of lung cancer patients: a retrospective cohort study. Sci Rep 2020; 10:17432. [PMID: 33060705 PMCID: PMC7566617 DOI: 10.1038/s41598-020-74082-0] [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: 04/12/2020] [Accepted: 09/23/2020] [Indexed: 11/08/2022] Open
Abstract
Lung cancer is the leading cause of cancer death worldwide. The Xuanwei-Fuyuan (XF) region of Yunnan, China has a high incidence of lung cancer from coal-related pollution. Effort to raise public awareness screening for lung cancer has been ongoing. We retrospectively analyzed overall survival (OS) of lung cancer patients of a tertiary cancer center in Yunnan to investigate screening and regional residential status as predictive factors. Consecutive cases of newly diagnosed lung cancer were reviewed. The lung cancer cases diagnosed by screening were more likely to be early-staged and treated by surgery than those diagnosed not by screening. In patients diagnosed not by screening, XF residential status was a significant predictor of improved OS. Frailty model detected significant heterogeneity associated with region of residence in unscreened patients. Potential biases associated with screening were examined by Monte Carlo simulations and sensitivity analyses. Focused effort in cancer screening and increased public awareness of pollution-related lung cancer in XF might have led to early diagnosis and improved OS, and increased investment in health care resources in high risk areas may have produced additional unobserved factors that underlay the association of XF residential status with improved OS in patients diagnosed not by screening.
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Wu FZ, Huang YL, Wu YJ, Tang EK, Wu MT, Chen CS, Lin YP. Prognostic effect of implementation of the mass low-dose computed tomography lung cancer screening program: a hospital-based cohort study. Eur J Cancer Prev 2020; 29:445-451. [PMID: 32740170 DOI: 10.1097/cej.0000000000000569] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Low-dose computed tomography lung cancer screening aims to detect early-stage lung cancers in order to decrease the incidence of advanced-stage lung cancers and to reduce lung cancer mortality. We analyzed the time trends of lung cancer stage distribution and mortality rates after the gradual implementation of the low-dose computed tomography lung cancer screening in a hospital-based cohort. Using the hospital-based cancer registry data on lung cancer number and death from 2007 to 2014, we aim to evaluate the trends in stage distribution and mortality rate after the gradual implementation of low-dose computed tomography lung cancer screening program over recent years. From 2007 to 2014, overall 2542 cases of lung cancers were diagnosed according to hospital-based cancer registry. For the 1-year mortality rate, the mortality rate decreased gradually from 48.16 to 37.04% between 2007 and 2014. For the 5-year mortality rate, the mortality rate decreased gradually from 88.49 to 69.44% between 2007 and 2014. There was a gradual decrease in stage IV lung cancer with the corresponding sharp increase in stage I early lung cancer after following the implementation of the large volume of the low-dose computed tomography examination between the years 2011 and 2014. In conclusion, these results suggest that the gradual implementation of low-dose computed tomography lung screening program could lead to a remarkable decrease in lung cancer mortality and a remarkable stage shift in the trend over time in this hospital-based cohort.
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Affiliation(s)
- Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Yi-Luan Huang
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Department of Nursing, Shu-Zen Junior College of Medicine and Management
| | - Ming-Ting Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Yun-Pei Lin
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
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Hammer MM, Palazzo LL, Paquette A, Eckel AL, Jacobson FL, Barbosa EM, Kong CY. Cost-Effectiveness of Follow-Up for Subsolid Pulmonary Nodules in High-Risk Patients. J Thorac Oncol 2020; 15:1298-1305. [DOI: 10.1016/j.jtho.2020.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/21/2020] [Accepted: 03/01/2020] [Indexed: 12/01/2022]
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Gierada DS, Black WC, Chiles C, Pinsky PF, Yankelevitz DF. Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study. Radiol Imaging Cancer 2020; 2:e190058. [PMID: 32300760 PMCID: PMC7135238 DOI: 10.1148/rycan.2020190058] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/15/2019] [Accepted: 11/20/2019] [Indexed: 12/17/2022]
Abstract
Lung cancer remains the overwhelmingly greatest cause of cancer death in the United States, accounting for more annual deaths than breast, prostate, and colon cancer combined. Accumulated evidence since the mid to late 1990s, however, indicates that low-dose CT screening of high-risk patients enables detection of lung cancer at an early stage and can reduce the risk of dying from lung cancer. CT screening is now a recommended clinical service in the United States, subject to guidelines and reimbursement requirements intended to standardize practice and optimize the balance of benefits and risks. In this review, the evidence on the effectiveness of CT screening will be summarized and the current guidelines and standards will be described in the context of knowledge gained from lung cancer screening studies. In addition, an overview of the potential advances that may improve CT screening will be presented, and the need to better understand the performance in clinical practice outside of the research trial setting will be discussed. © RSNA, 2020.
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Affiliation(s)
- David S. Gierada
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - William C. Black
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - Caroline Chiles
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - Paul F. Pinsky
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
| | - David F. Yankelevitz
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St Louis, MO 63110 (D.S.G.); Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH (W.C.B.); Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC (C.C.); Division of Cancer Prevention, National Cancer Institute, Bethesda, Md (P.F.P.); and Department of Radiology, Mount Sinai School of Medicine, New York, NY (D.F.Y.)
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Wang H, Weng Q, Hui J, Fang S, Wu X, Mao W, Chen M, Zheng L, Wang Z, Zhao Z, Zhou L, Tu J, Xu M, Huang Y, Ji J. Value of TSCT Features for Differentiating Preinvasive and Minimally Invasive Adenocarcinoma From Invasive Adenocarcinoma Presenting as Subsolid Nodules Smaller Than 3 cm. Acad Radiol 2020; 27:395-403. [PMID: 31201034 DOI: 10.1016/j.acra.2019.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 04/28/2019] [Accepted: 05/08/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND To distinguish preinvasive (adenocarcinoma in situ/atypical adenomatous hyperplasia) and minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IA) appearing as solitary subsolid nodules (SSNs) less than 3 cm based on thin-section computed tomography (TSCT) features to guide therapeutic approaches. METHODS A total of 154 lesions that were histopathologically confirmed to have pre/minimally invasive adenocarcinoma (hereafter pre/MIA) and IA presenting as part-solid nodules (PSNs) or pure ground-glass nodules (pGGNs) were retrospectively reviewed. The TSCT features, including diameter, area, CT value, shape, air bronchogram, margins, and location, were compared and assessed. Receiver operating characteristic analyses were conducted to determine the cut-off values for the qualitative variables and their diagnostic performances. RESULTS Of 154 nodules, 89 IA, 53 MIA, eight adenocarcinoma in situ, and four atypical adenomatous hyperplasia lesions were found. Univariate and multivariate logistic regression of the pre/MIA and IA lesions were compared and analyzed among PSNs and pGGNs. Among pGGNs, a significant difference was found in the area (p = 0.004, odds ratio [OR] = 0.124, 95% confidence interval [CI] = 0.300-0.515) between the pre/MIA and IA groups. In PSNs, significant differences were found in the diameter (p = 0.001, OR = 0.171, 95% CI = 0.063-0.467) and CT value (p = 0.001, OR = 0.996, 95% CI = 0.993-0.998) between the pre/MIA and IA groups. According to the corresponding receiver operating characteristic curves, the optimal cut-off tumor area in pGGNs to differentiate pre/MIA from IA was 0.595 cm2. A higher CT value of the lesion (≥ -298.500 HU) and a larger diameter (≥1.450 cm) in PSNs were significantly associated with IA. CONCLUSION Imaging features from TSCT contribute to distinguishing pre/MIA from IA in solitary subsolid nodules and may contribute to guide the clinical management of these lesions.
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Affiliation(s)
- Hailin Wang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Junguo Hui
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Shiji Fang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Xulu Wu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Weibo Mao
- Department of Pathology, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Liyun Zheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Zufei Wang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Zhongwei Zhao
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Limin Zhou
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Jianfei Tu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China
| | - Yuan Huang
- Department of Pathology, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, China.
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, Zhejiang, 323000, China.
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Differences in lung cancer characteristics and mortality rate between screened and non-screened cohorts. Sci Rep 2019; 9:19386. [PMID: 31852960 PMCID: PMC6920422 DOI: 10.1038/s41598-019-56025-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 12/05/2019] [Indexed: 12/17/2022] Open
Abstract
Screening programs for lung cancer aim to allow diagnosis at the early stage, and therefore the decline in mortality rates. Thus, the aim of this retrospective cohort study was to the comparison of screened and non-screened lung cancer in terms of lung cancer characteristics, overdiagnosis and survival rate. A retrospective study in which 2883 patients with 2883 lung cancer diagnosed according to the hospital-based lung cancer register database between 2007 and 2017. A comparison was performed in term of clinical characteristics and outcomes of lung cancer between the screened and non-screening patient groups. 2883 subjects were identified (93 screened and 2790 non-screened). Screened group patients were younger (59.91 ± 8.14 versus 67.58 ± 12.95; p < 0.0001), and were more likely to be female than non-screened group (61.3% versus 36.8%; p < 0.0001). The screened group showed significantly better outcomes in overall mortality than the non-screened group (10.75% versus 79.06%; <0.0001). In a Cox proportional hazard model, lung cancer in the screened group proved to be an independent prognostic factor in lung cancer subjects. Our findings point to the improved survival outcome in the screened group and might underline the benefit of low-dose computed tomography (LDCT) screening program in Asian populations with the high prevalence of non-smoking-related lung cancer. Further study aimed at the LDCT mass screening program targeting at light smokers and non-smoker outside of existing screening criteria is warranted.
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Qi L, Lu W, Yang L, Tang W, Zhao S, Huang Y, Wu N, Wang J. Qualitative and quantitative imaging features of pulmonary subsolid nodules: differentiating invasive adenocarcinoma from minimally invasive adenocarcinoma and preinvasive lesions. J Thorac Dis 2019; 11:4835-4846. [PMID: 31903274 DOI: 10.21037/jtd.2019.11.35] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To explore the role of qualitative and quantitative imaging features of pulmonary subsolid nodules (SSNs) in differentiating invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) and preinvasive lesions. Methods We reviewed the clinical records of our institute from October 2010 to December 2015 and included 316 resected SSNs from 287 patients: 260 pure ground-glass nodules, 47 part-solid nodules with solid components ≤5 mm, and 9 ground-glass nodules (GGNs) with cystic airspaces. According to the pathologic review results, 307 SSNs in addition to nine GGNs with cystic airspaces were divided into two groups: A, including atypical adenomatous hyperplasia (AAH) (n=15), adenocarcinoma in situ (AIS) (n=56), and MIA (n=41); B, including 195 IACs. Univariate and binary logistic regression analyses were conducted to identify independent risk factors for IAC. Results Univariate analysis showed significant differences between groups regarding patient age, mean diameter, mean and relative computed tomography (CT) values, volume, mass (all P<0.001), and morphological features including lobulated sign (P<0.001), spiculated sign (P=0.028), vacuole sign/air bronchogram (P<0.001), and pleural retraction (P=0.017). Binary logistic regression and receiver operating characteristic analysis indicated the SSN mass as the only independent risk factor of IAC (odds ratio, 1.007; P<0.001), with an optimal cutoff value of 283.2 mg [area under curve (AUC): 0.859; sensitivity: 68.7%; specificity: 92.9%]. Among lepidic, acinar, and papillary adenocarcinomas, we found significant differences for the vacuole sign/air bronchogram (P=0.032) and mean and relative CT values (P<0.001). All nine GGNs with cystic airspaces were IACs. Conclusions The SSN mass with an optimal cutoff value of 283.2 mg may be reliable for differentiating IAC from MIA and preinvasive lesions.
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Affiliation(s)
- Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wenwen Lu
- Department of Ophthalmology, Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing 100191, China
| | - Lin Yang
- Department of Diagnostic Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wei Tang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shijun Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yao Huang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ning Wu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.,PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Triphuridet N, Henschke C. Landscape on CT screening for lung cancer in Asia. LUNG CANCER (AUCKLAND, N.Z.) 2019; 10:107-124. [PMID: 31686936 PMCID: PMC6777900 DOI: 10.2147/lctt.s192643] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 08/28/2019] [Indexed: 12/24/2022]
Abstract
Lung cancer remains the leading cause of cancer incidence and mortality worldwide. Approximately 60% of the world's new cases of lung cancer and deaths from it are expected in Asia in 2018. Currently, lung cancer screening using low-dose computed tomography (LDCT) is recommended for heavy smokers in North America, Europe and some countries in Asia. Tobacco smoking being the major risk factor for lung cancer, but in Asia, lung cancer in never-smokers (LCINS) is also a concern. This paper reviews on lung cancer incidence, mortality, etiology, smoking in Asia, and systematic reviews on LDCT lung cancer screening studies, including ongoing projects and recommendation on lung cancer screening in Asia. Some of the earliest studies of LDCT lung cancer screening worldwide were in Asia. Many countries in Asia have developed LDCT screening studies in various high-risk participants. Currently, there are several ongoing large-scale lung cancer screening trials to evaluate the efficacy of LDCT screening for never-smokers and light smokers, as well as heavy smokers, and to evaluate the feasibility of population-based LDCT lung cancer screening.
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Affiliation(s)
- Natthaya Triphuridet
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Claudia Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Liang CH, Liu YC, Wu MT, Garcia-Castro F, Alberich-Bayarri A, Wu FZ. Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice. Clin Radiol 2019; 75:38-45. [PMID: 31521323 DOI: 10.1016/j.crad.2019.08.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 08/14/2019] [Indexed: 01/01/2023]
Abstract
AIM To test the diagnostic performance of a deep learning-based system for the detection of clinically significant pulmonary nodules/masses on chest radiographs. MATERIALS AND METHODS Using a retrospective study of 100 patients (47 with clinically significant pulmonary nodules/masses and 53 control subjects without pulmonary nodules), two radiologists verified clinically significantly pulmonary nodules/masses according to chest computed tomography (CT) findings. A computer-aided diagnosis (CAD) software using a deep-learning approach was used to detect pulmonary nodules/masses to determine the diagnostic performance in four algorithms (heat map, abnormal probability, nodule probability, and mass probability). RESULTS A total of 100 cases were included in the analysis. Among the four algorithms, mass algorithm could achieve a 76.6% sensitivity (36/47, 11 false negative) and 88.68% specificity (47/53, six false-positive) in the detection of pulmonary nodules/masses at the optimal probability score cut-off of 0.2884. Compared to the other three algorithms, mass probability algorithm had best predictive ability for pulmonary nodule/mass detection at the optimal probability score cut-off of 0.2884 (AUCMass: 0.916 versus AUCHeat map: 0.682, p<0.001; AUCMass: 0.916 versus AUCAbnormal: 0.810, p=0.002; AUCMass: 0.916 versus AUCNodule: 0.813, p=0.014). CONCLUSION In conclusion, the deep-learning based computer-aided diagnosis system will likely play a vital role in the early detection and diagnosis of pulmonary nodules/masses on chest radiographs. In future applications, these algorithms could support triage workflow via double reading to improve sensitivity and specificity during the diagnostic process.
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Affiliation(s)
- C-H Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Y-C Liu
- Department of Diagnostic Radiology, Xiamen Chang Gung Hospital, China
| | - M-T Wu
- Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan; Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - F Garcia-Castro
- Radiology Department, Hospital Universitarioy Polite'cnico La Fe and Biomedical Imaging Research Group (GIBI230), Valencia, Spain; QUIBIM SL, Valencia, Spain
| | - A Alberich-Bayarri
- Radiology Department, Hospital Universitarioy Polite'cnico La Fe and Biomedical Imaging Research Group (GIBI230), Valencia, Spain; QUIBIM SL, Valencia, Spain
| | - F-Z Wu
- Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan; Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
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Naidich DP. Low Dose Lung CT Screening in an Asian Population. Acad Radiol 2018; 25:1237-1239. [PMID: 30017500 DOI: 10.1016/j.acra.2018.06.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 06/06/2018] [Indexed: 02/06/2023]
Affiliation(s)
- David P Naidich
- Department of Radiology, New York University-Langone Medical Center, Center for Biological Imaging, 660 1st Ave, New York, NY 10016.
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Tang EK, Chen CS, Wu CC, Wu MT, Yang TL, Liang HL, Hsu HT, Wu FZ. Natural History of Persistent Pulmonary Subsolid Nodules: Long-Term Observation of Different Interval Growth. Heart Lung Circ 2018; 28:1747-1754. [PMID: 30268516 DOI: 10.1016/j.hlc.2018.08.015] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 06/05/2018] [Accepted: 08/27/2018] [Indexed: 11/28/2022]
Abstract
BACKGROUND The long-term natural course and outcomes of subsolid nodules (SSNs) in terms of true growth, substantial growth, and stage shift need to be clarified. METHODS Between 2002 and 2016, 128 subjects with persistent SSNs of 3cm or smaller were enrolled. The baseline and interval changes in the series computed tomography (CT) findings during the follow-up period were subsequently reviewed. RESULTS The mean follow-up period was 3.57±2.93years. The cumulative percentage of growth nodules of the part-solid nodule (PSN) group was significantly higher than that of the ground-glass nodule (GGN) group by Kaplan-Meier estimation (all p<0.0001). For true SSN growth, GGNs usually take a median follow-up of 7 years to grow; PSNs usually take a median follow-up of 3 years to grow. For substantial SSN growth, GGNs usually take a median follow-up of 9 years to grow; PSNs usually take a median follow-up of 3 years to grow. For stage shift, GGNs usually take a median follow-up of 12 years to grow; PSNs usually take a median follow-up of 9 years to grow. CONCLUSIONS The natural course in terms of true growth, substantial growth, and stage shift differed significantly according to their nodule type, which could contribute to the development of follow-up guidelines and management strategy of pulmonary SSNs.
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Affiliation(s)
- En-Kuei Tang
- Division of Thoracic Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Carol C Wu
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ming-Ting Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, 81362, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Tseng-Lung Yang
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, 81362, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Huei-Lung Liang
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, 81362, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Hui-Ting Hsu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, 81362, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, 81362, Taiwan; Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan.
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35
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Wu FZ, Kuo PL, Wu CC, Wu MT. The impact of patients' preferences on the decision of low-dose computed tomography lung cancer screening. Transl Lung Cancer Res 2018; 7:S236-S238. [PMID: 30393611 DOI: 10.21037/tlcr.2018.08.17] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
| | - Pei-Lun Kuo
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Carol C Wu
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ming-Ting Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan
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