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Feng X, Goodley P, Alcala K, Guida F, Kaaks R, Vermeulen R, Downward GS, Bonet C, Colorado-Yohar SM, Albanes D, Weinstein SJ, Goldberg M, Zins M, Relton C, Langhammer A, Skogholt AH, Johansson M, Robbins HA. Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis. Lancet Digit Health 2024; 6:e614-e624. [PMID: 39179310 PMCID: PMC11369914 DOI: 10.1016/s2589-7500(24)00123-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 03/08/2024] [Accepted: 06/06/2024] [Indexed: 08/26/2024]
Abstract
BACKGROUND Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts. METHODS We analysed 240 137 participants aged 45-80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London-Death (UCLD), the University College London-Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) criteria. FINDINGS Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59-0·77) to 0·83 (0·78-0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57-0·72) to 0·78 (0·74-0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%-79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON. INTERPRETATION Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria. FUNDING US National Cancer Institute, l'Institut National du Cancer, Cancer Research UK.
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Affiliation(s)
- Xiaoshuang Feng
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Patrick Goodley
- Division of Immunology, Immunity to Infection and Respiratory Medicine, University of Manchester, Manchester, UK; Manchester Thoracic Oncology Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Karine Alcala
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Florence Guida
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Rudolf Kaaks
- Department of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Roel Vermeulen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands; Department of Population Health Sciences, Institute for Risk Assessment Sciences, University of Utrecht, Utrecht, Netherlands
| | - George S Downward
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands; Department of Population Health Sciences, Institute for Risk Assessment Sciences, University of Utrecht, Utrecht, Netherlands
| | - Catalina Bonet
- Nutrition and Cancer Group, Epidemiology, Public Health, Cancer Prevention and Palliative Care Program, Bellvitge Biomedical Research Institute, L'Hospitalet de Llobregat, Barcelona, Spain; Unit of Nutrition and Cancer, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barecelona, Spain
| | - Sandra M Colorado-Yohar
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain; Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Stephanie J Weinstein
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Marcel Goldberg
- Population-based Epidemiological Cohorts Unit, INSERM UMS 11, Villejuif, France; Paris Cité University, Paris, France
| | - Marie Zins
- Population-based Epidemiological Cohorts Unit, INSERM UMS 11, Villejuif, France; Paris Cité University, Paris, France
| | - Caroline Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; School of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Arnulf Langhammer
- HUNT Research Center, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Anne Heidi Skogholt
- Department of Public Health and Nursing, KG Jebsen Centre for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Hilary A Robbins
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
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Li CC, Manella J, Kefi SE, Matthews AK. Does the revised LDCT lung cancer screening guideline bridge the racial disparities gap: Results from the health and retirement study. J Natl Med Assoc 2024; 116:180-188. [PMID: 38245469 DOI: 10.1016/j.jnma.2024.01.008] [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: 08/01/2023] [Revised: 10/17/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024]
Abstract
PURPOSE This study examined racial/ethnic disparities in lung cancer screening eligibility rates using 2013 US Preventive Services Task Force (USPSTF) guidelines for lung cancer with low-dose computed tomography (LDCT) and the revised 2021 guidelines. METHODS The study utilized a retrospective and cross-sectional research design by analyzing data from the Health and Retirement Study (HRS). N = 2,823 respondents aged 50-80 who self-reported current smoking were included in the analyses. Binary logistic regression analysis was conducted to examine the changed status of LDCT screening eligibility based on the revised 2021 guidelines by race/ethnicity after adjusting for respondent demographics. RESULTS Our study found substantial increases in screening eligibility rates across racial and ethnic groups when comparing the original and revised guidelines. The largest increase was observed among Black people (174%), Hispanics (152%), those in the other category (118%), and Whites who smoke (80.8%). When comparing original screening guidelines to revised guidelines, Whites who smoke had the highest percentage of changes from "not eligible" to "eligible" (28.3%), followed by individuals in the "other" category (28.1%), Black people (23.2%) and Hispanics who smoke (18.3%) (p < 0.001). Binary logistic regression results further showed that Black people who smoke (OR = 0.71, p = 0.001), as well as Hispanics who smoke (OR=0.54, p < 0.001), were less likely to change from not eligible to eligible for screening compared to Whites who smoke after adopting the revised screening guidelines. Based on the absolute differences in screening eligibility rates between Whites and other racial/ethnic groups, the disparities may have widened under the new guidelines, particularly with larger absolute differences observed between Whites, Black people, and Hispanics. CONCLUSIONS Our study highlights racial/ethnic disparities in LDCT screening eligibility among people who currently smoke. While the revised USPSTF guidelines increased screening eligibility for racial and ethnic minorities, they did not eliminate these disparities and may have widened under the new guidelines. Targeted interventions and policies are necessary to address barriers faced by underrepresented populations and promote equitable access to lung cancer screening.
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Affiliation(s)
- Chien-Ching Li
- Rush University, Department of Health Systems Management, Chicago, Illinois, USA.
| | - Jason Manella
- Endeavor Health, Department of Orthopaedics, Skokie, Illinois, USA
| | - Safa El Kefi
- Columbia University, School of Nursing, Department of Research and Scholarship, New York , NY, USA
| | - Alicia K Matthews
- Columbia University, School of Nursing, Department of Research and Scholarship, New York , NY, USA
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Yang JJ, Wen W, Zahed H, Zheng W, Lan Q, Abe SK, Rahman MS, Islam MR, Saito E, Gupta PC, Tamakoshi A, Koh WP, Gao YT, Sakata R, Tsuji I, Malekzadeh R, Sugawara Y, Kim J, Ito H, Nagata C, You SL, Park SK, Yuan JM, Shin MH, Kweon SS, Yi SW, Pednekar MS, Kimura T, Cai H, Lu Y, Etemadi A, Kanemura S, Wada K, Chen CJ, Shin A, Wang R, Ahn YO, Shin MH, Ohrr H, Sheikh M, Blechter B, Ahsan H, Boffetta P, Chia KS, Matsuo K, Qiao YL, Rothman N, Inoue M, Kang D, Robbins HA, Shu XO. Lung Cancer Risk Prediction Models for Asian Ever-Smokers. J Thorac Oncol 2024; 19:451-464. [PMID: 37944700 PMCID: PMC11126207 DOI: 10.1016/j.jtho.2023.11.002] [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: 07/26/2023] [Revised: 10/19/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Although lung cancer prediction models are widely used to support risk-based screening, their performance outside Western populations remains uncertain. This study aims to evaluate the performance of 11 existing risk prediction models in multiple Asian populations and to refit prediction models for Asians. METHODS In a pooled analysis of 186,458 Asian ever-smokers from 19 prospective cohorts, we assessed calibration (expected-to-observed ratio) and discrimination (area under the receiver operating characteristic curve [AUC]) for each model. In addition, we developed the "Shanghai models" to better refine risk models for Asians on the basis of two well-characterized population-based prospective cohorts and externally validated them in other Asian cohorts. RESULTS Among the 11 models, the Lung Cancer Death Risk Assessment Tool yielded the highest AUC (AUC [95% confidence interval (CI)] = 0.71 [0.67-0.74] for lung cancer death and 0.69 [0.67-0.72] for lung cancer incidence) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model had good calibration overall (expected-to-observed ratio [95% CI] = 1.06 [0.90-1.25]). Nevertheless, these models substantially underestimated lung cancer risk among Asians who reported less than 10 smoking pack-years or stopped smoking more than or equal to 20 years ago. The Shanghai models were found to have marginal improvement overall in discrimination (AUC [95% CI] = 0.72 [0.69-0.74] for lung cancer death and 0.70 [0.67-0.72] for lung cancer incidence) but consistently outperformed the selected Western models among low-intensity smokers and long-term quitters. CONCLUSIONS The Shanghai models had comparable performance overall to the best existing models, but they improved much in predicting the lung cancer risk of low-intensity smokers and long-term quitters in Asia.
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Affiliation(s)
- Jae Jeong Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Surgery, University of Florida College of Medicine, Gainesville, Florida; University of Florida Health Cancer Center, Gainesville, Florida
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hana Zahed
- International Agency for Research on Cancer, Lyon, France
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Sarah K Abe
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Md Shafiur Rahman
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan; Research Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Md Rashedul Islam
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan; Hitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan
| | - Eiko Saito
- Institute for Global Health Policy Research, National Center for Global Health and Medicine, Tokyo, Japan
| | - Prakash C Gupta
- Healis - Sekhsaria Institute for Public Health Mahaleb, Navi Mumbai, India
| | - Akiko Tamakoshi
- Department of Public Health, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A∗STAR), Singapore, Singapore
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People's Republic of China
| | - Ritsu Sakata
- Radiation Effects Research Foundation, Hiroshima, Japan
| | - Ichiro Tsuji
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Reza Malekzadeh
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yumi Sugawara
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Jeongseon Kim
- Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea
| | - Hidemi Ito
- Division of Cancer Information and Control, Department of Preventive Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan; Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Chisato Nagata
- Department of Epidemiology and Preventive Medicine, Gifu University Graduate School of Medicine, Gifu, Japan
| | - San-Lin You
- School of Medicine & Big Data Research Center, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Myung-Hee Shin
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Sang-Wook Yi
- Department of Preventive Medicine and Public Health, Catholic Kwandong University College of Medicine, Gangneung, Republic of Korea
| | - Mangesh S Pednekar
- Healis - Sekhsaria Institute for Public Health Mahaleb, Navi Mumbai, India
| | - Takashi Kimura
- Department of Public Health, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Yukai Lu
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Arash Etemadi
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Seiki Kanemura
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Keiko Wada
- Department of Epidemiology and Preventive Medicine, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Chien-Jen Chen
- Genomics Research Center, Academia Sinica, Taipei City, Taiwan
| | - Aesun Shin
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Renwei Wang
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yoon-Ok Ahn
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Heechoul Ohrr
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Mahdi Sheikh
- International Agency for Research on Cancer, Lyon, France
| | - Batel Blechter
- Division of Cancer Epidemiology and Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Illinois
| | - Paolo Boffetta
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, New York; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Keitaro Matsuo
- Division Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan; Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - You-Lin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Manami Inoue
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | | | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.
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Wang N, Yao C, Luo C, Liu S, Wu L, Hu W, Zhang Q, Rong Y, Yuan C, Wang F. Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer. Clin Chem Lab Med 2023; 61:2216-2228. [PMID: 37387637 DOI: 10.1515/cclm-2023-0291] [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: 03/20/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVES Non-small cell lung cancer (NSCLC) accounts for more than 80 % of all lung cancers, and its 5-year survival rate can be greatly improved by early diagnosis. However, early diagnosis remains elusive because of the lack of effective biomarkers. In this study, we aimed to develop an effective diagnostic model for NSCLC based on a combination of circulating biomarkers. METHODS Tissue-deregulated long noncoding RNAs (lncRNAs) in NSCLC were identified in datasets retrieved from the Gene Expression Omnibus (GEO, n=727) and The Cancer Genome Atlas (TCGA, n=1,135) databases, and their differential expression was verified in paired local plasma and exosome samples from NSCLC patients. Subsequently, LASSO regression was used to screen for biomarkers in a large clinical population, and a logistic regression model was used to establish a multi-marker diagnostic model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), clinical impact curves, and integrated discrimination improvement (IDI) were used to evaluate the efficiency of the diagnostic model. RESULTS Three lncRNAs-PGM5-AS1, SFTA1P, and CTA-384D8.35 were consistently expressed in online tissue datasets, plasma, and exosomes from local patients. LASSO regression identified nine variables (Plasma CTA-384D8.35, Plasma PGM5-AS1, Exosome CTA-384D8.35, Exosome PGM5-AS1, Exosome SFTA1P, Log10CEA, Log10CA125, SCC, and NSE) in clinical samples that were eventually included in the multi-marker diagnostic model. Logistic regression analysis revealed that Plasma CTA-384D8.35, exosome SFTA1P, Log10CEA, Exosome CTA-384D8.35, SCC, and NSE were independent risk factors for NSCLC (p<0.01), and their results were visualized using a nomogram to obtain personalized prediction outcomes. The constructed diagnostic model demonstrated good NSCLC prediction ability in both the training and validation sets (AUC=0.97). CONCLUSIONS In summary, the constructed circulating lncRNA-based diagnostic model has good NSCLC prediction ability in clinical samples and provides a potential diagnostic tool for NSCLC.
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Affiliation(s)
- Na Wang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Cong Yao
- Health Care Department, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Changliang Luo
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Department of Laboratory Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, P.R. China
| | - Shaoping Liu
- Medical Science Research Center, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Long Wu
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, P.R. China
| | - Weidong Hu
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Qian Zhang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Yuan Rong
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Chunhui Yuan
- Department of Laboratory Medicine, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, P.R. China
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Susai CJ, Velotta JB, Sakoda LC. Clinical Adjuncts to Lung Cancer Screening: A Narrative Review. Thorac Surg Clin 2023; 33:421-432. [PMID: 37806744 PMCID: PMC10926946 DOI: 10.1016/j.thorsurg.2023.03.002] [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] [Indexed: 10/10/2023]
Abstract
The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.
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Affiliation(s)
- Cynthia J Susai
- UCSF East Bay General Surgery, 1411 East 31st Street QIC 22134, Oakland, CA 94612, USA
| | - Jeffrey B Velotta
- Department of Thoracic Surgery, Kaiser Permanente Northern California, 3600 Broadway, Oakland, CA 94611, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA.
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Senthil P, Kuhan S, Potter AL, Jeffrey Yang CF. Update on Lung Cancer Screening Guideline. Thorac Surg Clin 2023; 33:323-331. [PMID: 37806735 DOI: 10.1016/j.thorsurg.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Lung cancer screening has been shown to reduce lung cancer mortality and is recommended for individuals meeting age and smoking history criteria. Despite the expansion of lung cancer screening guidelines in 2021, racial/ethnic and sex disparities persist. High-risk racial minorities and women are more likely to be diagnosed with lung cancer at younger ages and have lower smoking histories when compared with White and male counterparts, resulting in higher rates of ineligibility for screening. Risk prediction models, biomarkers, and deep learning may help refine the selection of individuals who would benefit from screening.
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Affiliation(s)
- Priyanka Senthil
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Sangkavi Kuhan
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Alexandra L Potter
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Chi-Fu Jeffrey Yang
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
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Jacobsen KK, Kobylecki CJ, Skov-Jeppesen SM, Bojesen SE. Development and validation of a simple general population lung cancer risk model including AHRR-methylation. Lung Cancer 2023; 181:107229. [PMID: 37150141 DOI: 10.1016/j.lungcan.2023.107229] [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: 03/07/2023] [Revised: 04/27/2023] [Accepted: 04/29/2023] [Indexed: 05/09/2023]
Abstract
INTRODUCTION Screening reduces lung cancer mortality of high-risk populations. Currently proposed screening eligibility criteria only identify half of those individuals, who later develop lung cancer. This study aimed to develop and validate a sensitive and simple model for predicting 10-year lung cancer risk. METHODS Using the 1991-94 examination of The Copenhagen City Heart Study in Denmark, 6,820 former or current smokers from the general population were followed for lung cancer within 10 years after examination. Logistic regression of baseline variables (age, sex, education, chronic obstructive pulmonary disease, family history of lung cancer, smoking status and cumulative smoking, secondhand smoking, occupational exposures to dust and fume, body mass index, lung function, plasma C-reactive protein, and AHRR(cg05575921) methylation) identified the best predictive model. The model was validated among 3,740 former or current smokers from the 2001-03 examination, also followed for 10 years. A simple risk chart was developed with Poisson regression. RESULTS Age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation identified 65 of 88 individuals who developed lung cancer in the validation cohort. The highest risk group, consisting of less educated men aged >65 with current smoking status and cumulative smoking >20 pack-years, had absolute 10-year risks varying from 4% to 16% by AHRR(cg05575921) methylation. CONCLUSION A simple risk chart including age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation, identifies individuals with 10-year lung cancer risk from below 1% to 16%. Including AHRR(cg05575921) methylation in the eligibility criteria for screening identifies smokers who would benefit the most from screening.
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Affiliation(s)
- Katja Kemp Jacobsen
- Department of Technology, Faculty of Health and Technology, University College Copenhagen, Copenhagen, Denmark
| | - Camilla Jannie Kobylecki
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Sune Moeller Skov-Jeppesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Stig Egil Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark; The Copenhagen City Heart Study, Copenhagen University Hospital, Frederiksberg and Bispebjerg Hospital, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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8
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Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet 2023; 401:390-408. [PMID: 36563698 DOI: 10.1016/s0140-6736(22)01694-4] [Citation(s) in RCA: 102] [Impact Index Per Article: 102.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/26/2022] [Accepted: 08/25/2022] [Indexed: 12/24/2022]
Abstract
Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.
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Affiliation(s)
- Scott J Adams
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Emily Stone
- Faculty of Medicine, University of New South Wales and Department of Lung Transplantation and Thoracic Medicine, St Vincent's Hospital, Sydney, NSW, Australia
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Pyng Lee
- Division of Respiratory and Critical Care Medicine, National University Hospital and National University of Singapore, Singapore
| | - Florian J Fintelmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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9
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Guo L, Meng Q, Zheng L, Chen Q, Liu Y, Xu H, Kang R, Zhang L, Liu S, Sun X, Zhang S. Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study. JMIR Public Health Surveill 2023; 9:e41640. [PMID: 36607729 PMCID: PMC9862335 DOI: 10.2196/41640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/04/2022] [Accepted: 11/25/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. OBJECTIVE The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. METHODS Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. RESULTS In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. CONCLUSIONS We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.
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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, China
| | - Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Liyang Zheng
- 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, China
| | - Qiong Chen
- 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, China
| | - Yin Liu
- 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, China
| | - Huifang Xu
- 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, China
| | - Ruihua Kang
- 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, China
| | - Luyao Zhang
- 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, China
| | - Shuzheng Liu
- 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, China
| | - Xibin Sun
- 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, China
| | - Shaokai Zhang
- 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, China
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10
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Chien LH, Chen TY, Chen CH, Chen KY, Hsiao CF, Chang GC, Tsai YH, Su WC, Huang MS, Chen YM, Chen CY, Liang SK, Chen CY, Wang CL, Hung HH, Jiang HF, Hu JW, Rothman N, Lan Q, Liu TW, Chen CJ, Yang PC, Chang IS, Hsiung CA. Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan. Cancer Epidemiol Biomarkers Prev 2022; 31:2208-2218. [PMID: 36129788 PMCID: PMC9720426 DOI: 10.1158/1055-9965.epi-22-0281] [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: 05/17/2022] [Revised: 07/20/2022] [Accepted: 09/20/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Methods synthesizing multiple data sources without prospective datasets have been proposed for absolute risk model development. This study proposed methods for adapting risk models for another population without prospective cohorts, which would help alleviate the health disparities caused by advances in absolute risk models. To exemplify, we adapted the lung cancer risk model PLCOM2012, well studied in the west, for Taiwan. METHODS Using Taiwanese multiple data sources, we formed an age-matched case-control study of ever-smokers (AMCCSE), estimated the number of ever-smoking lung cancer patients in 2011-2016 (NESLP2011), and synthesized a dataset resembling the population of cancer-free ever-smokers in 2010 regarding the PLCOM2012 risk factors (SPES2010). The AMCCSE was used to estimate the overall calibration slope, and the requirement that NESLP2011 equals the estimated total risk of individuals in SPES2010 was used to handle the calibration-in-the-large problem. RESULTS The adapted model PLCOT-1 (PLCOT-2) had an AUC of 0.78 (0.75). They had high performance in calibration and clinical usefulness on subgroups of SPES2010 defined by age and smoking experience. Selecting the same number of individuals for low-dose computed tomography screening using PLCOT-1 (PLCOT-2) would have identified approximately 6% (8%) more lung cancers than the US Preventive Services Task Forces 2021 criteria. Smokers having 40+ pack-years had an average PLCOT-1 (PLCOT-2) risk of 3.8% (2.6%). CONCLUSIONS The adapted PLCOT models had high predictive performance. IMPACT The PLCOT models could be used to design lung cancer screening programs in Taiwan. The methods could be applicable to other cancer models.
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Affiliation(s)
- Li-Hsin Chien
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Tzu-Yu Chen
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Chung-Hsing Chen
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Kuan-Yu Chen
- Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.,Taiwan Lung Cancer Tissue/Specimen Information Resource Center, National Health Research Institutes, Zhunan, Taiwan
| | - Gee-Chen Chang
- School of Medicine and Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.,Institute of Biomedical Sciences, National Chung Hsing University, Taichung, Taiwan.,Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ying-Huang Tsai
- Department of Respiratory Therapy, Chang Gung University, Taoyuan, Taiwan.,Department of Pulmonary and Critical Care, Xiamen Chang Gung Hospital, Xiamen, China
| | - Wu-Chou Su
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ming-Shyan Huang
- Department of Internal Medicine, E-Da Cancer Hospital, School of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chih-Yi Chen
- Institute of Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.,Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Sheng-Kai Liang
- Department of Internal Medicine, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan.,Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Chung-Yu Chen
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan
| | - Chih-Liang Wang
- Department of Pulmonary and Critical Care, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Hsiao-Han Hung
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Hsin-Fang Jiang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Jia-Wei Hu
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Tsang-Wu Liu
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Chien-Jen Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Pan-Chyr Yang
- Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan.,Corresponding Authors: Chao A. Hsiung, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36120; Fax: 375-86467; E-mail: ; and I-Shou Chang, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36130; E-mail:
| | - Chao A. Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.,Corresponding Authors: Chao A. Hsiung, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36120; Fax: 375-86467; E-mail: ; and I-Shou Chang, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36130; E-mail:
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11
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Bhardwaj M, Schöttker B, Holleczek B, Brenner H. Comparison of discrimination performance of 11 lung cancer risk models for predicting lung cancer in a prospective cohort of screening-age adults from Germany followed over 17 years. Lung Cancer 2022; 174:83-90. [PMID: 36356492 DOI: 10.1016/j.lungcan.2022.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/02/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
Randomized trials have demonstrated considerable reduction in lung cancer (LC) mortality by screening pre-selected heavy smokers with low-dose computed tomography (LDCT). Newer screening guidelines recommend refined LC risk models for selecting the target population for screening. We aimed to evaluate and compare the discrimination performance of LC risk models and previously used trial criteria in predicting LC incidence and mortality in a large German cohort of screening-age adults. Within ESTHER, a population-based prospective cohort study conducted in Saarland, Germany, 4812 ever smokers aged 50-75 years were followed up with respect to LC incidence and mortality for up to 17 years. We quantified the performance of 11 different LC risk models by the area under the curve (AUC) and compared the proportion of correctly predicted LC cases between the best performing models and the LDCT trial criteria. Risk prediction of LC incidence in the ESTHER ever smokers was best for the Bach model, LCRAT and LCDRAT with AUCs ranging from 0.782 to 0.787, from 0.770 to 0.774, and from 0.765 to 0.771 for the follow-up time periods of cases identified at 6, 11, and 17 years, respectively. At cutoffs yielding comparable positivity rates as the LDCT trial criteria, these models would have identified between 11.8 (95% CI 3.0-20.5) and 17.6 (95% CI 10.1-25.2) percent units higher proportions of LC cases occurring during the initial 6 years of follow-up. Use of LC risk models is expected to result in substantially greater potential to identify people at highest risk of LC, suggesting enhanced potential for reducing LC mortality by LC screening.
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Affiliation(s)
- Megha Bhardwaj
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Network Aging Research, University of Heidelberg, Bergheimer Strasse 20, 69115 Heidelberg, Germany
| | - Bernd Holleczek
- Saarland Cancer Registry, Präsident-Baltz-Strasse 5, 66119 Saarbrücken, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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12
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Grolleau E, de Bermont J, Devun F, Pérol D, Lacoste V, Delastre L, Fleurisson F, Devouassoux G, Mornex JF, Cotton F, Darrason M, Tammemagi M, Couraud S. Eligibility to lung cancer screening among staffs of a university hospital: A large cross-sectional survey. Respir Med Res 2022; 83:100970. [PMID: 36724677 DOI: 10.1016/j.resmer.2022.100970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/26/2022] [Accepted: 10/24/2022] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Implementation of Lung cancer screening (LCS) programs is challenging. The ILYAD study objectives is to evaluate communication methods to improve participation rate among the Lyon University Hospital employees. In this first part of the study, we aimed to determinate the number of eligible individuals among our population of employees. METHOD In November 2020, we conducted a questionnaire based cross sectional survey among the Lyon University Hospital employees (N = 26,954). We evaluated the PLCO m2012 risk prediction model and the eligibility criteria recommended by French guidelines. We assessed the proportion of eligible individuals among the responders and calculated the total eligible individuals in our hospital. RESULTS Overall, 4,526 questionnaires were available for analysis. 16.0% were current smokers, and 28.2% were former smokers. Among the 50-75yo ever-smoker employees, 27% were eligible according to the French guidelines, 2.7% of all eversmokers according to a PLCO m2012 score ≥ 1.51%, and thus, 3.8% of the surveyed population were eligible to the combined criteria. The factors associated with higher eligibility among 50-75yo ever-smokers were educational level, feeling symptoms related to tobacco smoking, personal history of COPD and family history of lung cancer. Using the French guidelines criteria only, we estimated the total number of eligible individuals in the hospital at 838. CONCLUSION In this study, we determined a theoretical number of eligible employees to LCS in our institution and the factors associated to eligibility. Secondly, we will propose LCS to all eligible employees of Lyon University Hospital with incremented information actions.
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Affiliation(s)
- Emmanuel Grolleau
- Service de pneumologie aigue et cancérologie thoracique, Hôpital Lyon Sud, Hospices Civils de Lyon, 69310, Pierre Bénite, France; Centre d'Innovation en Cancérologie de Lyon EA 3738, Faculté de médecine Lyon-Sud, Université Lyon 1, 69600, Oullins, France
| | - Julie de Bermont
- Service de pneumologie aigue et cancérologie thoracique, Hôpital Lyon Sud, Hospices Civils de Lyon, 69310, Pierre Bénite, France.
| | - Flavien Devun
- Unité de Recherche Commune en Oncologie Thoracique, Hospices Civils de Lyon, Lyon, France
| | - David Pérol
- Bureau d'études cliniques, Centre Léon Bérard, Lyon, France
| | - Véronique Lacoste
- Applied Linguistics Research Center, Lyon 2 university, Lyon, France
| | - Loïc Delastre
- Medical Management Department, Hospices Civils de Lyon, Lyon, France
| | - Fanny Fleurisson
- Medical Management Department, Hospices Civils de Lyon, Lyon, France
| | - Gilles Devouassoux
- Service de Pneumologie, Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France
| | - Jean-François Mornex
- Service de Pneumologie, Hôpital Louis Pradel, Hospices Civils de Lyon, Lyon, France
| | - François Cotton
- Service d'Imagerie Médicale, Hôpital Lyon Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Marie Darrason
- Service de pneumologie aigue et cancérologie thoracique, Hôpital Lyon Sud, Hospices Civils de Lyon, 69310, Pierre Bénite, France; Institut de Recherche en Philosophie, Lyon 3 University, Lyon, France
| | | | - Sébastien Couraud
- Service de pneumologie aigue et cancérologie thoracique, Hôpital Lyon Sud, Hospices Civils de Lyon, 69310, Pierre Bénite, France; Centre d'Innovation en Cancérologie de Lyon EA 3738, Faculté de médecine Lyon-Sud, Université Lyon 1, 69600, Oullins, France; Unité de Recherche Commune en Oncologie Thoracique, Hospices Civils de Lyon, Lyon, France
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13
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Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe? Diagnostics (Basel) 2022; 12:diagnostics12102326. [PMID: 36292015 PMCID: PMC9601268 DOI: 10.3390/diagnostics12102326] [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: 08/06/2022] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/24/2022] Open
Abstract
Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.
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14
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Yu H, Raut JR, Bhardwaj M, Zhang Y, Sandner E, Schöttker B, Holleczek B, Schrotz-King P, Brenner H. A serum microRNA signature for enhanced selection of people for lung cancer screening. CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 42:1222-1225. [PMID: 35929101 PMCID: PMC9648391 DOI: 10.1002/cac2.12346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/18/2022] [Accepted: 07/26/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Haixin Yu
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, 69120, Baden-Württemberg, Germany
| | - Janhavi R Raut
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, 69120, Baden-Württemberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Megha Bhardwaj
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Evelin Sandner
- Pneumology and Thoracic Oncology, Robert-Bosch Krankenhaus, Klinik Schillerhoehe, Gerlingen, 70839, Baden-Württemberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,Network Aging Research, University of Heidelberg, Bergheimer Straße 20, Heidelberg, 69115, Baden-Württemberg, Germany
| | - Bernd Holleczek
- Saarland Cancer Registry, Präsident-Baltz-Straße 5, Saarbrucken, 66119, Saarland, Germany
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, 69120, Baden-Württemberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany
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15
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Wood DE, Kazerooni EA, Aberle D, Berman A, Brown LM, Eapen GA, Ettinger DS, Ferguson JS, Hou L, Kadaria D, Klippenstein D, Kumar R, Lackner RP, Leard LE, Lennes IT, Leung ANC, Mazzone P, Merritt RE, Midthun DE, Onaitis M, Pipavath S, Pratt C, Puri V, Raz D, Reddy C, Reid ME, Sandler KL, Sands J, Schabath MB, Studts JL, Tanoue L, Tong BC, Travis WD, Wei B, Westover K, Yang SC, McCullough B, Hughes M. NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022. J Natl Compr Canc Netw 2022; 20:754-764. [PMID: 35830884 DOI: 10.6004/jnccn.2022.0036] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The NCCN Guidelines for Lung Cancer Screening recommend criteria for selecting individuals for screening and provide recommendations for evaluation and follow-up of lung nodules found during initial and subsequent screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.
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Affiliation(s)
- Douglas E Wood
- Fred Hutchinson Cancer Research Center/Seattle Cancer Care Alliance
| | | | | | - Abigail Berman
- Abramson Cancer Center at the University of Pennsylvania
| | | | | | | | | | - Lifang Hou
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University
| | - Dipen Kadaria
- St. Jude Children's Research Hospital/The University of Tennessee Health Science Center
| | | | | | | | | | | | | | - Peter Mazzone
- Case Comprehensive Cancer Center/University Hospitals Seidman Cancer Center and Cleveland Clinic Taussig Cancer Institute
| | - Robert E Merritt
- The Ohio State University Comprehensive Cancer Center - James Cancer Hospital and Solove Research Institute
| | | | - Mark Onaitis
- Fred Hutchinson Cancer Research Center/Seattle Cancer Care Alliance
| | | | | | - Varun Puri
- Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine
| | - Dan Raz
- City of Hope National Medical Center
| | | | | | | | - Jacob Sands
- Dana-Farber/Brigham and Women's Cancer Center
| | | | | | | | | | | | | | | | - Stephen C Yang
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins
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16
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Ngo PJ, Wade S, Vaneckova P, Behar-Harpaz S, Caruana M, Cressman S, Tammemagi M, Karikios D, Canfell K, Weber M. Health utilities for participants in a population-based sample who meet eligibility criteria for lung cancer screening. Lung Cancer 2022; 169:47-54. [DOI: 10.1016/j.lungcan.2022.05.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/05/2022] [Accepted: 05/10/2022] [Indexed: 12/17/2022]
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Fahrmann JF, Marsh T, Irajizad E, Patel N, Murage E, Vykoukal J, Dennison JB, Do KA, Ostrin E, Spitz MR, Lam S, Shete S, Meza R, Tammemägi MC, Feng Z, Hanash SM. Blood-Based Biomarker Panel for Personalized Lung Cancer Risk Assessment. J Clin Oncol 2022; 40:876-883. [PMID: 34995129 PMCID: PMC8906454 DOI: 10.1200/jco.21.01460] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/26/2021] [Accepted: 12/10/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To investigate whether a panel of circulating protein biomarkers would improve risk assessment for lung cancer screening in combination with a risk model on the basis of participant characteristics. METHODS A blinded validation study was performed using prostate lung colorectal ovarian (PLCO) Cancer Screening Trial data and biospecimens to evaluate the performance of a four-marker protein panel (4MP) consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in combination with a lung cancer risk prediction model (PLCOm2012) compared with current US Preventive Services Task Force (USPSTF) screening criteria. The 4MP was assayed in 1,299 sera collected preceding lung cancer diagnosis and 8,709 noncase sera. RESULTS The 4MP alone yielded an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77 to 0.82) for case sera collected within 1-year preceding diagnosis and 0.74 (95% CI, 0.72 to 0.76) among the entire specimen set. The combined 4MP + PLCOm2012 model yielded an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.82 to 0.88) for case sera collected within 1 year preceding diagnosis. The benefit of the 4MP in the combined model resulted from improvement in sensitivity at high specificity. Compared with the USPSTF2021 criteria, the combined 4MP + PLCOm2012 model exhibited statistically significant improvements in sensitivity and specificity. Among PLCO participants with ≥ 10 smoking pack-years, the 4MP + PLCOm2012 model would have identified for annual screening 9.2% more lung cancer cases and would have reduced referral by 13.7% among noncases compared with USPSTF2021 criteria. CONCLUSION A blood-based biomarker panel in combination with PLCOm2012 significantly improves lung cancer risk assessment for lung cancer screening.
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Affiliation(s)
- Johannes F. Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Tracey Marsh
- Biostatistics Program, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Ehsan Irajizad
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Nikul Patel
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Eunice Murage
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jody Vykoukal
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jennifer B. Dennison
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Kim-Anh Do
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Edwin Ostrin
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Stephen Lam
- Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Sanjay Shete
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, School of Public Health, Ann Arbor, MI
| | - Martin C. Tammemägi
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada
- Department of Health Sciences, Brock University, St Catharines, Ontario, Canada
| | - Ziding Feng
- Biostatistics Program, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Samir M. Hanash
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX
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Jacobsen KK, Schnohr P, Jensen GB, Bojesen SE. AHRR (cg5575921) methylation safely improves specificity of lung cancer screening eligibility criteria: A cohort study. Cancer Epidemiol Biomarkers Prev 2022; 31:758-765. [DOI: 10.1158/1055-9965.epi-21-1059] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 01/04/2022] [Indexed: 11/16/2022] Open
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China. Front Oncol 2022; 11:766939. [PMID: 35059311 PMCID: PMC8764453 DOI: 10.3389/fonc.2021.766939] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China. Methods A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set. Results A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk. Conclusions We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.
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Affiliation(s)
- Lan-Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui-Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu-Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiao-Qin Cao
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xi-Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Gong Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Shao-Kai Zhang,
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Aredo JV, Choi E, Ding VY, Tammemägi MC, ten Haaf K, Luo SJ, Freedman ND, Wilkens LR, Le Marchand L, Wakelee HA, Meza R, Park SSL, Cheng I, Han SS. OUP accepted manuscript. JNCI Cancer Spectr 2022; 6:6583194. [PMID: 35642317 PMCID: PMC9156850 DOI: 10.1093/jncics/pkac033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/05/2022] [Accepted: 03/04/2022] [Indexed: 11/12/2022] Open
Abstract
Background Methods Results Conclusions
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Affiliation(s)
- Jacqueline V Aredo
- Department of Medicine, University of California, San Francisco, CA, USA
- Stanford University School of Medicine, Stanford, CA, USA
| | - Eunji Choi
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Martin C Tammemägi
- Department of Health Sciences, Brock University, St. Catharines, ON, Canada
| | - Kevin ten Haaf
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sophia J Luo
- Stanford University School of Medicine, Stanford, CA, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Lynne R Wilkens
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Loïc Le Marchand
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Heather A Wakelee
- Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Rafael Meza
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sung-Shim Lani Park
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Summer S Han
- Stanford University School of Medicine, Stanford, CA, USA
- Correspondence to: Summer S. Han, PhD, Stanford University School of Medicine, 1701 Page Mill Rd, Room 234, Stanford, CA 94304, USA (e-mail: )
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21
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Tammemägi MC, Ruparel M, Tremblay A, Myers R, Mayo J, Yee J, Atkar-Khattra S, Yuan R, Cressman S, English J, Bedard E, MacEachern P, Burrowes P, Quaife SL, Marshall H, Yang I, Bowman R, Passmore L, McWilliams A, Brims F, Lim KP, Mo L, Melsom S, Saffar B, Teh M, Sheehan R, Kuok Y, Manser R, Irving L, Steinfort D, McCusker M, Pascoe D, Fogarty P, Stone E, Lam DCL, Ng MY, Vardhanabhuti V, Berg CD, Hung RJ, Janes SM, Fong K, Lam S. USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study. Lancet Oncol 2022; 23:138-148. [PMID: 34902336 PMCID: PMC8716337 DOI: 10.1016/s1470-2045(21)00590-8] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/01/2021] [Accepted: 10/08/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Lung cancer is a major health problem. CT lung screening can reduce lung cancer mortality through early diagnosis by at least 20%. Screening high-risk individuals is most effective. Retrospective analyses suggest that identifying individuals for screening by accurate prediction models is more efficient than using categorical age-smoking criteria, such as the US Preventive Services Task Force (USPSTF) criteria. This study prospectively compared the effectiveness of the USPSTF2013 and PLCOm2012 model eligibility criteria. METHODS In this prospective cohort study, participants from the International Lung Screening Trial (ILST), aged 55-80 years, who were current or former smokers (ie, had ≥30 pack-years smoking history or ≤15 quit-years since last permanently quitting), and who met USPSTF2013 criteria or a PLCOm2012 risk threshold of at least 1·51% within 6 years of screening, were recruited from nine screening sites in Canada, Australia, Hong Kong, and the UK. After enrolment, patients were assessed with the USPSTF2013 criteria and the PLCOm2012 risk model with a threshold of at least 1·70% at 6 years. Data were collected locally and centralised. Main outcomes were the comparison of lung cancer detection rates and cumulative life expectancies in patients with lung cancer between USPSTF2013 criteria and the PLCOm2012 model. In this Article, we present data from an interim analysis. To estimate the incidence of lung cancers in individuals who were USPSTF2013-negative and had PLCOm2012 of less than 1·51% at 6 years, ever-smokers in the Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO) who met these criteria and their lung cancer incidence were applied to the ILST sample size for the mean follow-up occurring in the ILST. This trial is registered at ClinicalTrials.gov, NCT02871856. Study enrolment is almost complete. FINDINGS Between June 17, 2015, and Dec 29, 2020, 5819 participants from the International Lung Screening Trial (ILST) were enrolled on the basis of meeting USPSTF2013 criteria or the PLCOm2012 risk threshold of at least 1·51% at 6 years. The same number of individuals was selected for the PLCOm2012 model as for the USPSTF2013 criteria (4540 [78%] of 5819). After a mean follow-up of 2·3 years (SD 1·0), 135 lung cancers occurred in 4540 USPSTF2013-positive participants and 162 in 4540 participants included in the PLCOm2012 of at least 1·70% at 6 years group (cancer sensitivity difference 15·8%, 95% CI 10·7-22·1%; absolute odds ratio 4·00, 95% CI 1·89-9·44; p<0·0001). Compared to USPSTF2013-positive individuals, PLCOm2012-selected participants were older (mean age 65·7 years [SD 5·9] vs 63·3 years [5·7]; p<0·0001), had more comorbidities (median 2 [IQR 1-3] vs 1 [1-2]; p<0·0001), and shorter life expectancy (13·9 years [95% CI 12·8-14·9] vs 14·8 [13·6-16·0] years). Model-based difference in cumulative life expectancies for those diagnosed with lung cancer were higher in those who had PLCOm2012 risk of at least 1·70% at 6 years than individuals who were USPSTF2013-positive (2248·6 years [95% CI 2089·6-2425·9] vs 2000·7 years [1841·2-2160·3]; difference 247·9 years, p=0·015). INTERPRETATION PLCOm2012 appears to be more efficient than the USPSTF2013 criteria for selecting individuals to enrol into lung cancer screening programmes and should be used for identifying high-risk individuals who benefit from the inclusion in these programmes. FUNDING Terry Fox Research Institute, The UBC-VGH Hospital Foundation and the BC Cancer Foundation, the Alberta Cancer Foundation, the Australian National Health and Medical Research Council, Cancer Research UK and a consortium of funders, and the Roy Castle Lung Cancer Foundation for the UK Lung Screen Uptake Trial.
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Affiliation(s)
- Martin C Tammemägi
- Department of Health Sciences, Brock University, St Catharines, ON, Canada.
| | - Mamta Ruparel
- Lungs for Living, UCL Respiratory, Department of Medicine, University College London, London, UK
| | - Alain Tremblay
- Division of Respiratory Medicine & Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Renelle Myers
- BC Cancer Research Centre, Integrative Oncology, Vancouver, BC, Canada; Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - John Mayo
- Department of Radiology, Vancouver, BC, Canada
| | - John Yee
- Department of Thoracic Surgery, Vancouver, BC, Canada
| | | | - Ren Yuan
- Vancouver Coastal Health, Vancouver, BC, Canada; Department of Radiology, BC Cancer, Vancouver, BC, Canada
| | - Sonya Cressman
- Centre for Epidemiology and Evaluation, SFU, Burnaby, BC, Canada
| | | | - Eric Bedard
- Department of Surgery, University of Alberta, Edmonton, AB, Canada
| | - Paul MacEachern
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Paul Burrowes
- Department of Diagnostic Imaging, Foothills Medical Center, Calgary, AB, Canada
| | - Samantha L Quaife
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Henry Marshall
- The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
| | - Ian Yang
- The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
| | - Rayleen Bowman
- The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
| | - Linda Passmore
- The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
| | - Annette McWilliams
- Department of Respiratory Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Fraser Brims
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, WA, Australia; Curtin Medical School, National Centre for Asbestos Related Diseases, Institute for Respiratory Health, Perth, WA, Australia
| | - Kuan Pin Lim
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Lin Mo
- Royal Darwin Hospital, Tiwi, NT, Australia
| | - Stephen Melsom
- Department of Medical Imaging, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Bann Saffar
- Department of Medical Imaging, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Mark Teh
- Department of Medical Imaging, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Ramon Sheehan
- Department of Medical Imaging, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Yijin Kuok
- Department of Medical Imaging, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Renee Manser
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Louis Irving
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Daniel Steinfort
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mark McCusker
- Department of Radiology, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Diane Pascoe
- Department of Radiology, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Paul Fogarty
- Epworth Internal Medicine Clinical Institute, Melbourne VIC, Australia
| | - Emily Stone
- St Vincent's Hospital, Kinghorn Cancer Centre, University of New South Wales, Sydney, NSW, Australia
| | - David C L Lam
- Department of Medicine, University of Hong Kong, Hong Kong
| | - Ming-Yen Ng
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong
| | | | | | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Samuel M Janes
- Lungs for Living, UCL Respiratory, Department of Medicine, University College London, London, UK
| | - Kwun Fong
- The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
| | - Stephen Lam
- BC Cancer Research Centre, Integrative Oncology, Vancouver, BC, Canada
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22
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China. Lung Cancer 2021; 163:27-34. [PMID: 34894456 DOI: 10.1016/j.lungcan.2021.11.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer. MATERIALS AND METHODS Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively. RESULTS By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups. CONCLUSIONS We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.
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Affiliation(s)
- Lan-Wei 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 450008, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Li-Yang Zheng
- 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 450008, China
| | - Qiong Chen
- 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 450008, China
| | - Yin Liu
- 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 450008, China
| | - Hui-Fang Xu
- 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 450008, China
| | - Rui-Hua Kang
- 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 450008, China
| | - Lu-Yao Zhang
- 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 450008, China
| | - Xiao-Qin Cao
- 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 450008, China
| | - Shu-Zheng Liu
- 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 450008, China
| | - Xi-Bin Sun
- 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 450008, China
| | - Jian-Gong Zhang
- 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 450008, China
| | - Shao-Kai Zhang
- 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 450008, China.
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23
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O'Dowd EL, Ten Haaf K, Kaur J, Duffy SW, Hamilton W, Hubbard RB, Field JK, Callister ME, Janes SM, de Koning HJ, Rawlinson J, Baldwin DR. Selection of eligible participants for screening for lung cancer using primary care data. Thorax 2021; 77:882-890. [PMID: 34716280 DOI: 10.1136/thoraxjnl-2021-217142] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 09/21/2021] [Indexed: 12/23/2022]
Abstract
Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown. METHOD The Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLPv2) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCOm2012) models. Lung cancer occurrence over 5-6 years was measured in ever-smokers aged 50-80 years and compared with 5-year (LLPv2) and 6-year (PLCOm2012) predicted risk. RESULTS Over 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLPV2 produced a c-statistic of 0.700 (0.694-0.710), but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCOm2012 showed similar performance (c-statistic: 0.679 (0.673-0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLPv2) and 0.15% (PLCOm2012), would avoid contacting 42.7% and 27.4% of ever-smokers who did not develop lung cancer for screening eligibility assessment, at the cost of missing 15.6% and 11.4% of lung cancers. CONCLUSION Risk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data.
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Affiliation(s)
- Emma L O'Dowd
- Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Kevin Ten Haaf
- Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jaspreet Kaur
- Department of Epidemiology, University of Nottingham School of Medicine, Nottingham, UK
| | - Stephen W Duffy
- Wolfson Institute of Preventive Medicine, Barts and London, London, UK
| | | | - Richard B Hubbard
- Department of Epidemiology, University of Nottingham School of Medicine, Nottingham, UK
| | - John K Field
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, University of Liverpool, Liverpool, UK
| | | | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | | | | | - David R Baldwin
- City Campus, Nottingham University Hospitals NHS Trust, Nottingham, UK
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24
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Yeh MCH, Wang YH, Yang HC, Bai KJ, Wang HH, Li YCJ. Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach. J Med Internet Res 2021; 23:e26256. [PMID: 34342588 PMCID: PMC8371476 DOI: 10.2196/26256] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/03/2021] [Accepted: 05/04/2021] [Indexed: 01/20/2023] Open
Abstract
Background Artificial intelligence approaches can integrate complex features and can be used to predict a patient’s risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. Objective The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer. Methods We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed. Results The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease. Conclusions Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.
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Affiliation(s)
- Marvin Chia-Han Yeh
- Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yu-Hsiang Wang
- School of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Hsuan-Chia Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Kuan-Jen Bai
- Division of Pulmonary Medicine, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Pulmonary Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Hsiao-Han Wang
- Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Department of Dermatology, School of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chuan Jack Li
- Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.,Department of Dermatology, School of Medicine, Taipei Medical University, Taipei, Taiwan
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25
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Lam S, Tammemagi M. Contemporary issues in the implementation of lung cancer screening. Eur Respir Rev 2021; 30:30/161/200288. [PMID: 34289983 DOI: 10.1183/16000617.0288-2020] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/08/2021] [Indexed: 12/24/2022] Open
Abstract
Lung cancer screening with low-dose computed tomography can reduce death from lung cancer by 20-24% in high-risk smokers. National lung cancer screening programmes have been implemented in the USA and Korea and are being implemented in Europe, Canada and other countries. Lung cancer screening is a process, not a test. It requires an organised programmatic approach to replicate the lung cancer mortality reduction and safety of pivotal clinical trials. Cost-effectiveness of a screening programme is strongly influenced by screening sensitivity and specificity, age to stop screening, integration of smoking cessation intervention for current smokers, screening uptake, nodule management and treatment costs. Appropriate management of screen-detected lung nodules has significant implications for healthcare resource utilisation and minimising harm from radiation exposure related to imaging studies, invasive procedures and clinically significant distress. This review focuses on selected contemporary issues in the path to implement a cost-effective lung cancer screening at the population level. The future impact of emerging technologies such as deep learning and biomarkers are also discussed.
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Affiliation(s)
- Stephen Lam
- British Columbia Cancer Agency, Vancouver, BC, Canada.,University of British Columbia, Vancouver, BC, Canada
| | - Martin Tammemagi
- Dept of Health Sciences, Brock University, St Catharines, ON, Canada
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26
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Sosa E, D’Souza G, Akhtar A, Sur M, Love K, Duffels J, Raz DJ, Kim JY, Sun V, Erhunmwunsee L. Racial and socioeconomic disparities in lung cancer screening in the United States: A systematic review. CA Cancer J Clin 2021; 71:299-314. [PMID: 34015860 PMCID: PMC8266751 DOI: 10.3322/caac.21671] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Nonsmall cell lung cancer (NSCLC) is the leading cause of cancer deaths. Lung cancer screening (LCS) reduces NSCLC mortality; however, a lack of diversity in LCS studies may limit the generalizability of the results to marginalized groups who face higher risk for and worse outcomes from NSCLC. Identifying sources of inequity in the LCS pipeline is essential to reduce disparities in NSCLC outcomes. The authors searched 3 major databases for studies published from January 1, 2010 to February 27, 2020 that met the following criteria: 1) included screenees between ages 45 and 80 years who were current or former smokers, 2) written in English, 3) conducted in the United States, and 4) discussed socioeconomic and race-based LCS outcomes. Eligible studies were assessed for risk of bias. Of 3721 studies screened, 21 were eligible. Eligible studies were evaluated, and their findings were categorized into 3 themes related to LCS disparities faced by Black and socioeconomically disadvantaged individuals: 1) eligibility; 2) utilization, perception, and utility; and 3) postscreening behavior and care. Disparities in LCS exist along racial and socioeconomic lines. There are several steps along the LCS pipeline in which Black and socioeconomically disadvantaged individuals miss the potential benefits of LCS, resulting in increased mortality. This study identified potential sources of inequity that require further investigation. The authors recommend the implementation of prospective trials that evaluate eligibility criteria for underserved groups and the creation of interventions focused on improving utilization and follow-up care to decrease LCS disparities.
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Affiliation(s)
- Ernesto Sosa
- Department of Populations Sciences, City of Hope National Medical Center
| | - Gail D’Souza
- Department of Surgery, City of Hope Comprehensive Cancer Center
| | - Aamna Akhtar
- Department of Surgery, City of Hope Comprehensive Cancer Center
| | - Melissa Sur
- Department of Populations Sciences, City of Hope National Medical Center
| | - Kyra Love
- Division of Library Services, City of Hope National Medical Center
| | - Jeanette Duffels
- Division of Library Services, City of Hope National Medical Center
| | - Dan J Raz
- Department of Surgery, City of Hope Comprehensive Cancer Center
| | - Jae Y Kim
- Department of Surgery, City of Hope Comprehensive Cancer Center
| | - Virginia Sun
- Department of Populations Sciences, City of Hope National Medical Center
- Department of Surgery, City of Hope Comprehensive Cancer Center
| | - Loretta Erhunmwunsee
- Department of Populations Sciences, City of Hope National Medical Center
- Department of Surgery, City of Hope Comprehensive Cancer Center
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27
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Robbins HA, Alcala K, Swerdlow AJ, Schoemaker MJ, Wareham N, Travis RC, Crosbie PAJ, Callister M, Baldwin DR, Landy R, Johansson M. Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom. Br J Cancer 2021; 124:2026-2034. [PMID: 33846525 PMCID: PMC8184952 DOI: 10.1038/s41416-021-01278-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/04/2021] [Accepted: 01/13/2021] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK. METHODS We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC). RESULTS Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%). CONCLUSION In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.
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Affiliation(s)
| | - Karine Alcala
- International Agency for Research on Cancer, Lyon, France
| | | | | | | | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | | | - David R Baldwin
- Nottingham University Hospitals and University of Nottingham, Nottingham, UK
| | - Rebecca Landy
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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28
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Ten Haaf K, van der Aalst CM, de Koning HJ, Kaaks R, Tammemägi MC. Personalising lung cancer screening: An overview of risk-stratification opportunities and challenges. Int J Cancer 2021; 149:250-263. [PMID: 33783822 PMCID: PMC8251929 DOI: 10.1002/ijc.33578] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/04/2021] [Accepted: 03/12/2021] [Indexed: 12/17/2022]
Abstract
Randomised clinical trials have shown the efficacy of computed tomography lung cancer screening, initiating discussions on whether and how to implement population‐based screening programs. Due to smoking behaviour being the primary risk‐factor for lung cancer and part of the criteria for determining screening eligibility, lung cancer screening is inherently risk‐based. In fact, the selection of high‐risk individuals has been shown to be essential in implementing lung cancer screening in a cost‐effective manner. Furthermore, studies have shown that further risk‐stratification may improve screening efficiency, allow personalisation of the screening interval and reduce health disparities. However, implementing risk‐based lung cancer screening programs also requires overcoming a number of challenges. There are indications that risk‐based approaches can negatively influence the trade‐off between individual benefits and harms if not applied thoughtfully. Large‐scale implementation of targeted, risk‐based screening programs has been limited thus far. Consequently, questions remain on how to efficiently identify and invite high‐risk individuals from the general population. Finally, while risk‐based approaches may increase screening program efficiency, efficiency should be balanced with the overall impact of the screening program. In this review, we will address the opportunities and challenges in applying risk‐stratification in different aspects of lung cancer screening programs, as well as the balance between screening program efficiency and impact.
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Affiliation(s)
- Kevin Ten Haaf
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Carlijn M van der Aalst
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Harry J de Koning
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Martin C Tammemägi
- Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada
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29
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Martini K, Chassagnon G, Frauenfelder T, Revel MP. Ongoing challenges in implementation of lung cancer screening. Transl Lung Cancer Res 2021; 10:2347-2355. [PMID: 34164282 PMCID: PMC8182720 DOI: 10.21037/tlcr-2021-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Lung cancer is the leading cause of cancer deaths in Europe and around the world. Although available therapies have undergone considerable development in the past decades, the five-year survival rate for lung cancer remains low. This sobering outlook results mainly from the advanced stages of cancer most patients are diagnosed with. As the population at risk is relatively well defined and early stage disease is potentially curable, lung cancer outcomes may be improved by screening. Several studies already show that lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality. However, for a successful implementation of LCS programmes, several challenges have to be overcome: selection of high-risk individuals, standardization of nodule classification and measurement, specific training of radiologists, optimization of screening intervals and screening duration, handling of ancillary findings are some of the major points which should be addressed. Last but not least, the psychological impact of screening on screened individuals and the impact of potential false positive findings should not be neglected. The aim of this review is to discuss the different challenges of implementing LCS programmes and to give some hints on how to overcome them. Finally, we will also discuss the psychological impact of screening on quality of life and the importance of smoking cessation.
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Affiliation(s)
- Katharina Martini
- Radiology Department, Hôpital Cochin, APHP.Centre-Université de Paris, Paris, France.,Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Guillaume Chassagnon
- Radiology Department, Hôpital Cochin, APHP.Centre-Université de Paris, Paris, France
| | - Thomas Frauenfelder
- Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Marie-Pierre Revel
- Radiology Department, Hôpital Cochin, APHP.Centre-Université de Paris, Paris, France
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30
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Tammemägi MC, Darling GE, Schmidt H, Llovet D, Buchanan DN, Leung Y, Miller B, Rabeneck L. Selection of individuals for lung cancer screening based on risk prediction model performance and economic factors - The Ontario experience. Lung Cancer 2021; 156:31-40. [PMID: 33887677 DOI: 10.1016/j.lungcan.2021.04.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/29/2021] [Accepted: 04/06/2021] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Randomized controlled trials have shown that screening with computed tomography reduces lung cancer mortality but is most effective when applied to high-risk individuals. Accurate lung cancer risk prediction models effectively select individuals for screening. Few pilots or programs have implemented risk models for enrolling individuals for screening in real-world, population-based settings. This report describes implementation of the PLCOm2012 risk prediction model in the Ontario Health (Cancer Care Ontario) lung cancer screening Pilot. METHODS In the Pilot's Health Technology Assessment, 576 categorical age/pack-years/quit-years scenarios were evaluated using MISCAN microsimulation modeling and cost-effectiveness analyses. A preferred model was selected which provided the most life-years gained per cost. The PLCOm2012 was compared to the preferred MISCAN scenario at a threshold that yielded the same number eligible (risk ≥2.0 %/6-years). RESULTS The PLCOm2012 had significantly higher sensitivity and predictive value (68.1 % vs 59.6 %, p < 0.0001; 4.90 % vs 4.29 %, p = 0.044), and an Expert Panel selected it for use in the Pilot. The Pilot cancer detection rate was significantly higher than in the NLST (p = 0.009) or NELSON (p = 0.003) and there was a significant shift to early stage compared to historical Ontario Cancer Registry statistics (p < 0.0001). Pre- and post-Pilot evaluations found that conducting quality risk assessments were not excessively time consuming or difficult, and participants' satisfaction was high. CONCLUSIONS The PLCOm2012 was efficiently implemented in the Pilot in a real-world setting and is being used to transition into a provincial program. Compared to categorical age/pack-years/quit-years criteria, risk assessment using the PLCOm2012 can lead to effective and efficient screening.
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Affiliation(s)
- Martin C Tammemägi
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada; Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada.
| | - Gail E Darling
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada; Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Heidi Schmidt
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada; Joint Department of Medical Imaging (JDMI) at University Health Network, Sinai Health, and Women's College Hospital, Toronto, Ontario, Canada; Division of Cardiothoracic Imaging, Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Diego Llovet
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Daniel N Buchanan
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada
| | - Yvonne Leung
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Beth Miller
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada
| | - Linda Rabeneck
- Prevention and Cancer Control, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario, Canada
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Development and Validation of a Risk Prediction Model for Esophageal Squamous Cell Carcinoma Using Cohort Studies. Am J Gastroenterol 2021; 116:683-691. [PMID: 33982937 DOI: 10.14309/ajg.0000000000001094] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Esophageal squamous cell carcinoma (ESCC) carries a poor prognosis, but earlier tumor detection would improve survival. We aimed to develop and externally validate a risk prediction model based on exposure to readily available risk factors to identify high-risk individuals of ESCC. METHODS Competing risk regression modeling was used to develop a risk prediction model. Individuals' absolute risk of ESCC during follow-up was computed with the cumulative incidence function. We used prospectively collected data from the Nord-Trøndelag Health Study (HUNT) for model derivation and the UK Biobank cohort for validation. Candidate predictors were age, sex, tobacco smoking, alcohol consumption, body mass index (BMI), education, cohabitation, physical exercise, and employment. Model performance was validated internally and externally by evaluating model discrimination using the area under the receiver-operating characteristic curve (AUC) and model calibration. RESULTS The developed risk prediction model included age, sex, smoking, alcohol, and BMI. The AUC for 5-year risk of ESCC was 0.76 (95% confidence interval [CI], 0.58-0.93) in the derivation cohort and 0.70 (95% CI, 0.64-0.75) in the validation cohort. The calibration showed close agreement between the predicted cumulative risk and observed probabilities of developing ESCC. Higher net benefit was observed when applying the risk prediction model than considering all participants as being at high risk, indicating good clinical usefulness. A web tool for risk calculation was developed: https://sites.google.com/view/escc-ugis-ki. DISCUSSION This ESCC risk prediction model showed good discrimination and calibration and validated well in an independent cohort. This readily available model can help select high-risk individuals for preventive interventions.
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Turner J, Pond GR, Tremblay A, Johnston M, Goss G, Nicholas G, Martel S, Bhatia R, Liu G, Schmidt H, Tammemagi MC, Puksa S, Atkar-Khattra S, Tsao MS, Lam S, Goffin JR. Risk Perception Among a Lung Cancer Screening Population. Chest 2021; 160:718-730. [PMID: 33667493 DOI: 10.1016/j.chest.2021.02.050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/28/2021] [Accepted: 02/03/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND A successful lung cancer screening program requires a patient cohort at sufficient risk of developing cancer who are willing to participate. Among other factors, a patient's lung cancer risk perception may inform their attitudes toward screening and smoking cessation programs. RESEARCH QUESTION This study analyzed data from the Pan-Canadian Early Detection of Lung Cancer (PanCan) Study to address the following questions: Which factors are associated with the perception of lung cancer risk? Is there an association between risk perception for lung cancer and actual calculated risk? Is there an association between risk perception for lung cancer and the intent to quit smoking? Are there potential targets for lung cancer screening awareness? STUDY DESIGN AND METHODS The PanCan study recruited current or former smokers aged 50 to 75 years who had at least a 2% risk of developing lung cancer over 6 years to undergo low-dose screening CT. Risk perception and worry about lung cancer were captured on a baseline questionnaire. Cumulative logistic regression analysis was used to assess the relationship between baseline risk variables and both lung cancer risk perception and worry. RESULTS Among the 2,514 individuals analyzed, a higher perceived risk of lung cancer was positively associated with calculated risk (P = .032). Younger age, being a former smoker, respiratory symptoms, lower FEV1, COPD, and a family history of lung cancer were associated with higher perceived risk. Conversely, a consistent relationship between calculated risk and worry was not identified. There was a positive association between risk perception and lung cancer worry and reported intent to quit smoking. INTERPRETATION Individuals' lung cancer risk perception correlated positively with calculated risk in a screening population. Promotion of screening programs may benefit from focusing on factors associated with higher risk perception; conversely, harnessing worry seemingly holds less value.
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Affiliation(s)
| | | | | | | | - Glen Goss
- University of Ottawa, Ottawa, ON, Canada
| | | | - Simon Martel
- Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Québec, QC, Canada
| | | | - Geoffrey Liu
- University Health Network and Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Heidi Schmidt
- University Health Network and Princess Margaret Cancer Centre, Toronto, ON, Canada
| | | | | | | | - Ming-Sound Tsao
- University Health Network and Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Stephen Lam
- British Columbia Cancer Agency, Vancouver, BC, Canada
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Performance of Risk Factor-Based Guidelines and Model-Based Chest CT Lung Cancer Screening in World Trade Center-Exposed Fire Department Rescue/Recovery Workers. Chest 2020; 159:2060-2071. [PMID: 33279511 DOI: 10.1016/j.chest.2020.11.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/20/2020] [Accepted: 11/28/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Lung cancer is a leading cause of cancer incidence and death in the United States. Risk factor-based guidelines and risk model-based strategies are used to identify patients who could benefit from low-dose chest CT (LDCT) screening. Few studies compare guidelines or models within the same cohort. We evaluate lung cancer screening performance of two risk factor-based guidelines (US Preventive Services Task Force 2014 recommendations [USPSTF-2014] and National Comprehensive Cancer Network Group 2 [NCCN-2]) and two risk model-based strategies, Prostate Lung Colorectal and Ovarian Cancer Screening (PLCOm2012) and the Bach model) in the same occupational cohort. RESEARCH QUESTION Which risk factor-based guideline or model-based strategy is most accurate in detecting lung cancers in a highly exposed occupational cohort? STUDY DESIGN AND METHODS Fire Department of City of New York (FDNY) rescue/recovery workers exposed to the September 11, 2001 attacks underwent LDCT lung cancer screening based on smoking history and age. The USPSTF-2014, NCCN-2, PLCOm2012 model, and Bach model were retrospectively applied to determine how many lung cancers were diagnosed using each approach. RESULTS Among the study population (N = 3,953), 930 underwent a baseline scan that met at least one risk factor or model-based LDCT screening strategy; 73% received annual follow-up scans. Among the 3,953, 63 lung cancers were diagnosed, of which 50 were detected by at least one LDCT screening strategy. The NCCN-2 guideline was the most sensitive (79.4%; 50/63). When compared with NCCN-2, stricter age and smoking criteria reduced sensitivity of the other guidelines/models (USPSTF-2014 [44%], PLCOm2012 [51%], and Bach[46%]). The 13 missed lung cancers were mainly attributable to smoking less and quitting longer than guideline/model eligibility criteria. False-positive rates were similar across all four guidelines/models. INTERPRETATION In this cohort, our findings support expanding eligibility for LDCT lung cancer screening by lowering smoking history from ≥30 to ≥20 pack-years and age from 55 years to 50 years old. Additional studies are needed to determine its generalizability to other occupational/environmental exposed cohorts.
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Delorme S, Kaaks R. Lung Cancer Screening by Low-Dose Computed Tomography: Part 2 - Key Elements for Programmatic Implementation of Lung Cancer Screening. ROFO-FORTSCHR RONTG 2020; 193:644-651. [PMID: 33212539 DOI: 10.1055/a-1290-7817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
PURPOSE For screening with low-dose CT (LDCT) to be effective, the benefits must outweigh the potential risks. In large lung cancer screening studies, a mortality reduction of approx. 20 % has been reported, which requires several organizational elements to be achieved in practice. MATERIALS AND METHODS The elements to be set up are an effective invitation strategy, uniform and quality-assured assessment criteria, and computer-assisted evaluation tools resulting in a nodule management algorithm to assign each nodule the needed workup intensity. For patients with confirmed lung cancer, immediate counseling and guideline-compliant treatment in tightly integrated regional expert centers with expert skills are required. First, pulmonology contacts as well as CT facilities should be available in the participant's neighborhood. IT infrastructure, linkage to clinical cancer registries, quality management as well as epidemiologic surveillance are also required. RESULTS An effective organization of screening will result in an articulated structure of both widely distributed pulmonology offices as the participants' primary contacts and CT facilities as well as central expert facilities for supervision of screening activities, individual clarification of suspicious findings, and treatment of proven cancer. CONCLUSION In order to ensure that the benefits of screening more than outweigh the potential harms and that it will be accepted by the public, a tightly organized structure is needed to ensure wide availability of pulmonologists as first contacts and CT facilities with expert skills and high-level equipment concentrated in central facilities. KEY POINTS · For lung cancer screening, elements must function optimally and be tightly organized.. · Lung cancer screening requires a network of expert centers and collaborating facilities.. · IT infrastructure, QM, epidemiological surveillance, and linkage to cancer registries are essential.. CITATION FORMAT · Delorme S, Kaaks R: Lung Cancer Screening by Low-Dose Computed Tomography: Part 2 - Key Elements for Programmatic Implementation of Lung Cancer Screening. Fortschr Röntgenstr 2021; 193: 644 - 651.
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Affiliation(s)
- Stefan Delorme
- Division of Radiology, German Cancer Research Centre, Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Centre, Heidelberg, Germany.,Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Germany
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Kaaks R, Delorme S. Lung Cancer Screening by Low-Dose Computed Tomography - Part 1: Expected Benefits, Possible Harms, and Criteria for Eligibility and Population Targeting. ROFO-FORTSCHR RONTG 2020; 193:527-536. [PMID: 33212540 DOI: 10.1055/a-1290-7926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Trials in the USA and Europe have convincingly demonstrated the efficacy of screening by low-dose computed tomography (LDCT) as a means to lower lung cancer mortality, but also document potential harms related to radiation, psychosocial stress, and invasive examinations triggered by false-positive screening tests and overdiagnosis. To ensure that benefits (lung cancer deaths averted; life years gained) outweigh the risk of harm, lung cancer screening should be targeted exclusively to individuals who have an elevated risk of lung cancer, plus sufficient residual life expectancy. METHODS AND CONCLUSIONS Overall, randomized screening trials show an approximate 20 % reduction in lung cancer mortality by LDCT screening. In view of declining residual life expectancy, especially among continuing long-term smokers, risk of being over-diagnosed is likely to increase rapidly above the age of 75. In contrast, before age 50, the incidence of LC may be generally too low for screening to provide a positive balance of benefits to harms and financial costs. Concise criteria as used in the NLST or NELSON trials may provide a basic guideline for screening eligibility. An alternative would be the use of risk prediction models based on smoking history, sex, and age as a continuous risk factor. Compared to concise criteria, such models have been found to identify a 10 % to 20 % larger number of LC patients for an equivalent number of individuals to be screened, and additionally may help provide security that screening participants will all have a high-enough LC risk to balance out harm potentially caused by radiation or false-positive screening tests. KEY POINTS · LDCT screening can significantly reduce lung cancer mortality. · Screening until the age of 80 was shown to be efficient in terms of cancer deaths averted; in terms of LYG relative to overdiagnosis, stopping at a younger age (e. g. 75) may have greater efficiency. · Risk models may improve the overall net benefit of lung cancer screening. CITATION FORMAT · Kaaks R, Delorme S. Lung Cancer Screening by Low-Dose Computed Tomography - Part 1: Expected Benefits, Possible Harms, and Criteria for Eligibility and Population Targeting. Fortschr Röntgenstr 2021; 193: 527 - 536.
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Affiliation(s)
- Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Centre, Heidelberg, Germany.,Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Germany
| | - Stefan Delorme
- Division of Radiology, German Cancer Research Centre, Heidelberg, Germany
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Usher-Smith J, Simmons RK, Rossi SH, Stewart GD. Current evidence on screening for renal cancer. Nat Rev Urol 2020; 17:637-642. [PMID: 32860009 PMCID: PMC7610655 DOI: 10.1038/s41585-020-0363-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2020] [Indexed: 02/07/2023]
Abstract
Renal cell carcinoma (RCC) incidence is increasing worldwide. A high proportion of individuals are asymptomatic at diagnosis, but RCC has a high mortality rate. These facts suggest that RCC meets some of the criteria for screening, and a new analysis shows that screening for RCC could potentially be cost-effective. Targeted screening of high-risk individuals is likely to be the most cost-effective strategy to maximize the benefits and reduce the harms of screening. However, the size of the benefit of earlier initiation of treatment and the overall cost-effectiveness of screening remains uncertain. The optimal screening modality and target population is also unclear, and uncertainties exist regarding the specification and implementation of a screening programme. Before moving to a fully powered trial of screening, future work should focus on the following: developing and validating accurate risk prediction models; developing non-invasive methods of early RCC detection; establishing the feasibility, public acceptability and potential uptake of screening; establishing the prevalence of RCC and stage distribution of RCC detected by screening; and evaluating the potential harms of screening, including the impact on quality of life, overdiagnosis and over-treatment.
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Affiliation(s)
- Juliet Usher-Smith
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Rebecca K Simmons
- Department of Public Health, Bartolins Allé 2, University of Aarhus, Aarhus C, Denmark
| | - Sabrina H Rossi
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK.
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Toumazis I, Bastani M, Han SS, Plevritis SK. Risk-Based lung cancer screening: A systematic review. Lung Cancer 2020; 147:154-186. [DOI: 10.1016/j.lungcan.2020.07.007] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/17/2022]
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Juchli F, Zangger M, Schueck A, von Wolff M, Stute P. Chronic non-communicable disease risk calculators - An overview, part I. Maturitas 2020; 143:25-35. [PMID: 33308633 DOI: 10.1016/j.maturitas.2020.07.009] [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: 04/30/2020] [Revised: 06/21/2020] [Accepted: 07/28/2020] [Indexed: 11/26/2022]
Abstract
This review identifies the different risk assessment tools that stratify the individual's risk of four of the eight leading causes of death in women: breast cancer, lung cancer, colorectal cancer and osteoporosis. It will be followed by the publication of a second paper that summarizes the risk assessment tools for the other four leading causes of death (myocardial infarction, stroke, diabetes mellitus type 2 and dementia). The different tools were compared by their use of different variables and validation criteria. To corroborate the validation process, validation study papers were considered for each risk assessment tool. Four tables, one for each illness, were designed. The tables provide an outline for each risk assessment tool, which includes its inventor/company, required variables, advantages, disadvantages and validity. These tables simplify the comparison of the different tools and enable the identification of the most suitable one for each patient.
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Affiliation(s)
- Fabienne Juchli
- Department of General Internal Medicine, Muri Hospital, Muri, Switzerland
| | - Martina Zangger
- Department of General Internal Medicine, Thun Hospital, Thun, Switzerland
| | - Andrea Schueck
- Department of Anesthesiology, Lachen Hospital, Lachen, Switzerland
| | - Michael von Wolff
- Department of Obstetrics and Gynecology, University Women's Hospital, Bern, Switzerland
| | - Petra Stute
- Department of Obstetrics and Gynecology, University Women's Hospital, Bern, Switzerland.
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Risk Prediction Model Versus United States Preventive Services Task Force Lung Cancer Screening Eligibility Criteria: Reducing Race Disparities. J Thorac Oncol 2020; 15:1738-1747. [PMID: 32822843 DOI: 10.1016/j.jtho.2020.08.006] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/02/2020] [Accepted: 08/04/2020] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Disparities exist in lung cancer outcomes between African American and white people. The current United States Preventive Services Task Force (USPSTF) lung cancer screening eligibility criteria, which is based solely on age and smoking history, may exacerbate racial disparities. We evaluated whether the PLCOm2012 risk prediction model more effectively selects African American ever-smokers for screening. METHODS Lung cancer cases diagnosed between 2010 and 2019 at an urban medical center serving a racially and ethnically diverse population were retrospectively reviewed for lung cancer screening eligibility based on the USPSTF criteria versus the PLCOm2012 model. RESULTS This cohort of 883 ever-smokers comprised the following racial and ethnic makeup: 258 white (29.2%), 497 African American (56.3%), 69 Hispanic (7.8%), 24 Asian (2.7%), and 35 other (4.0%). Compared with the USPSTF criteria, the PLCOm2012 model increased the sensitivity for the African American cohort at lung cancer risk thresholds of 1.51%, 1.70%, and 2.00% per 6 years (p < 0.0001). For example, at the 1.70% risk threshold, the PLCOm2012 model identified 71.3% African American cases, whereas the USPSTF criteria only identified 50.3% (p < 0.0001). In contrast, in case of whites there was no difference (66.0% versus 62.4%, respectively [p = 0.203]). Of the African American ever-smokers who were PLCO1.7%-positive and USPSTF-negative, the criteria missed from the USPSTF were those with pack-years less than 30 (67.7%), quit time of greater than 15 years (22.5%), and age less than 55 years (13.0%). CONCLUSIONS The PLCOm2012 model was found to be preferable over the USPSTF criteria at identifying African American ever-smokers for lung cancer screening. The broader use of this model in racially diverse populations may help overcome disparities in lung cancer screening and outcomes.
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Hüsing A, Kaaks R. Risk prediction models versus simplified selection criteria to determine eligibility for lung cancer screening: an analysis of German federal-wide survey and incidence data. Eur J Epidemiol 2020; 35:899-912. [PMID: 32594286 PMCID: PMC7524688 DOI: 10.1007/s10654-020-00657-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 06/16/2020] [Indexed: 12/19/2022]
Abstract
As randomized trials in the USA and Europe have convincingly demonstrated efficacy of lung cancer screening by computed tomography (CT), European countries are discussing the introduction of screening programs. To maintain acceptable cost-benefit and clinical benefit-to-harm ratios, screening should be offered to individuals at sufficiently elevated risk of having lung cancer. Using federal-wide survey and lung cancer incidence data (2008–2013), we examined the performance of four well-established risk models from the USA (PLCOM2012, LCRAT, Bach) and the UK (LLP2008) in the German population, comparing with standard eligibility criteria based on age limits, minimal pack years of smoking (or combination of total duration with average intensity) and maximum years since smoking cessation. The eligibility criterion recommended by the United States Preventive Services Taskforce (USPSTF) would select about 3.2 million individuals, a group equal in size to the upper fifth of ever smokers age 50–79 at highest risk, and to 11% of all adults aged 50–79. According to PLCOM2012, the model showing best concordance between numbers of lung cancer cases predicted and reported in registries, persons with 5-year risk ≥ 1.7% included about half of all lung cancer incidence in the full German population. Compared to eligibility criteria (e.g. USPSTF), risk models elected individuals in higher age groups, including ex-smokers with longer average quitting times. Further studies should address how in Germany these shifts may affect expected benefits of CT screening in terms of life-years gained versus the potential harm of age-specific increasing risk of over-diagnosis.
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Affiliation(s)
- Anika Hüsing
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Center for Lung Research (DZL), Translational Lung Research Center (TLRC), Heidelberg, Germany
| | - Rudolf Kaaks
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Center for Lung Research (DZL), Translational Lung Research Center (TLRC), Heidelberg, Germany
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Veronesi G, Baldwin DR, Henschke CI, Ghislandi S, Iavicoli S, Oudkerk M, De Koning HJ, Shemesh J, Field JK, Zulueta JJ, Horgan D, Fiestas Navarrete L, Infante MV, Novellis P, Murray RL, Peled N, Rampinelli C, Rocco G, Rzyman W, Scagliotti GV, Tammemagi MC, Bertolaccini L, Triphuridet N, Yip R, Rossi A, Senan S, Ferrante G, Brain K, van der Aalst C, Bonomo L, Consonni D, Van Meerbeeck JP, Maisonneuve P, Novello S, Devaraj A, Saghir Z, Pelosi G. Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe. Cancers (Basel) 2020; 12:E1672. [PMID: 32599792 PMCID: PMC7352874 DOI: 10.3390/cancers12061672] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 12/11/2022] Open
Abstract
Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.
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Affiliation(s)
- Giulia Veronesi
- Faculty of Medicine and Surgery—Vita-Salute San Raffaele University, 20132 Milan, Italy;
- Division of Thoracic Surgery, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - David R. Baldwin
- Department of Respiratory Medicine, David Evans Research Centre, Nottingham University Hospitals NHS Trust, Nottingham NG5 1PB, UK;
| | - Claudia I. Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (C.I.H.); (N.T.); (R.Y.)
| | - Simone Ghislandi
- Department of Social and Political Sciences, Bocconi University, 20136 Milan, Italy; (S.G.); (L.F.N.)
| | - Sergio Iavicoli
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers’ Compensation Authority (INAIL), 00078 Rome, Italy;
| | - Matthijs Oudkerk
- Center for Medical Imaging, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands;
| | - Harry J. De Koning
- Department of Public Health, Erasmus MC—University Medical Centre Rotterdam, 3015 GD Rotterdam, The Netherlands; (H.J.D.K.); (C.v.d.A.)
| | - Joseph Shemesh
- The Grace Ballas Cardiac Research Unit, Sheba Medical Center, Affiliated with the Sackler Faculty of Medicine, Tel-Aviv University, 52621 Tel Aviv-Yafo, Israel;
| | - John K. Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, The University of Liverpool, Liverpool L69 3BX, UK;
| | - Javier J. Zulueta
- Department of Pulmonology, Clinica Universidad de Navarra, 31008 Pamplona, Spain;
- Visiongate Inc., Phoenix, AZ 85044, USA
| | - Denis Horgan
- European Alliance for Personalised Medicine (EAPM), Avenue de l’Armée Legerlaan 10, 1040 Brussels, Belgium;
| | - Lucia Fiestas Navarrete
- Department of Social and Political Sciences, Bocconi University, 20136 Milan, Italy; (S.G.); (L.F.N.)
| | | | - Pierluigi Novellis
- Division of Thoracic Surgery, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Rachael L. Murray
- Division of Epidemiology and Public Health, UK Centre for Tobacco and Alcohol Studies, Clinical Sciences Building, City Hospital, University of Nottingham, Nottingham NG5 1PB, UK;
| | - Nir Peled
- The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka Medical Center & Ben-Gurion University, 84101 Beer-Sheva, Israel;
| | - Cristiano Rampinelli
- Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy;
| | - Gaetano Rocco
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Witold Rzyman
- Department of Thoracic Surgery, Medical University of Gdańsk, 80-210 Gdańsk, Poland;
| | | | - Martin C. Tammemagi
- Department of Health Sciences, Brock University, 1812 Sir Isaac Brock Way, St Catharines, ON L2S 3A1, Canada;
| | - Luca Bertolaccini
- Division of Thoracic Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy;
| | - Natthaya Triphuridet
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (C.I.H.); (N.T.); (R.Y.)
- Faculty of Medicine and Public Health, Chulabhorn Royal Academy, HRH Princess Chulabhorn College of Medical Science, Bangkok 10210, Thailand
| | - Rowena Yip
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (C.I.H.); (N.T.); (R.Y.)
| | - Alexia Rossi
- Department of Biomedical Sciences, Humanitas University, 20090 Pieve Emanuele (MI), Italy;
| | - Suresh Senan
- Department of Radiation Oncology, Amsterdam University Medical Centers, VU location, De Boelelaan 1117, Postbox 7057, 1007 MB Amsterdam, The Netherlands;
| | - Giuseppe Ferrante
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS, 20089 Rozzano (MI), Italy;
| | - Kate Brain
- Division of Population Medicine, School of Medicine, Cardiff University, Cardiff CF14 4YS, UK;
| | - Carlijn van der Aalst
- Department of Public Health, Erasmus MC—University Medical Centre Rotterdam, 3015 GD Rotterdam, The Netherlands; (H.J.D.K.); (C.v.d.A.)
| | - Lorenzo Bonomo
- Department of Bioimaging and Radiological Sciences, Catholic University, 00168 Rome, Italy;
| | - Dario Consonni
- Epidemiology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Jan P. Van Meerbeeck
- Thoracic Oncology, Antwerp University Hospital and Ghent University, 2650 Edegem, Belgium;
| | - Patrick Maisonneuve
- Division of Epidemiology and Biostatistics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy;
| | - Silvia Novello
- Department of Oncology, University of Torino, 10124 Torino, Italy; (G.V.S.); (S.N.)
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, London SW3 6NP, UK;
| | - Zaigham Saghir
- Department of Respiratory Medicine, Herlev-Gentofte University Hospital, 2900 Hellerup, Denmark;
| | - Giuseppe Pelosi
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Inter-Hospital Pathology Division, IRCCS MultiMedica, 20138 Milan, Italy
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Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe. Cancers (Basel) 2020; 12:0. [PMID: 32599792 PMCID: PMC7352874 DOI: 10.3390/cancers12060000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.
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Lyu Z, Li N, Chen S, Wang G, Tan F, Feng X, Li X, Wen Y, Yang Z, Wang Y, Li J, Chen H, Lin C, Ren J, Shi J, Wu S, Dai M, He J. Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population. Cancer Med 2020; 9:3983-3994. [PMID: 32253829 PMCID: PMC7286442 DOI: 10.1002/cam4.3025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 02/20/2020] [Accepted: 03/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population. METHODS During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with Pentry = .15 and Pstay = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability. RESULTS A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (PHL = .689) and all subgroups. CONCLUSIONS We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.
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Affiliation(s)
- Zhangyan Lyu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuohua Chen
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Gang Wang
- Health Department of Kailuan (Group), Tangshan, China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoshuang Feng
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Wen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuoyu Yang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yalong Wang
- Department of Thoracic Surgery, 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
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongda Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunqing Lin
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiansong Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jufang Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shouling Wu
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Min Dai
- Office of Cancer Screening, 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
- Department of Thoracic Surgery, 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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Teles GBDS, Macedo ACS, Chate RC, Valente VAT, Funari MBDG, Szarf G. LDCT lung cancer screening in populations at different risk for lung cancer. BMJ Open Respir Res 2020; 7:7/1/e000455. [PMID: 33371010 PMCID: PMC7011883 DOI: 10.1136/bmjresp-2019-000455] [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: 06/04/2019] [Revised: 12/16/2019] [Accepted: 01/04/2020] [Indexed: 12/23/2022] Open
Abstract
Introduction The improvement of low-dose CT (LDCT) lung cancer screening selection criteria could help to include more individuals who have lung cancer, or in whom lung cancer will develop, while avoiding significant cost increase. We evaluated baseline results of LDCT lung cancer screening in a population with a heterogeneous risk profile for lung cancer. Methods LDCT lung cancer screening was implemented alongside a preventive health programme in a private hospital in Brazil. Individuals older than 45 years, smokers and former smokers, regardless of tobacco exposure, were included. Patients were classified according to the National Lung Screening Trial (NLST) eligibility criteria and to PLCOm2012 6-year lung cancer risk. Patient characteristics, CT positivity rate, detection rate of lung cancer and false-positive rate were assessed. Results LDCT scans of 472 patients were evaluated and three lung adenocarcinomas were diagnosed. CT positivity rate (Lung-RADS 3/4) was significantly higher (p=0.019) in the NLST group (10.1% (95% CI, 5.9% to 16.9%)) than in the non-NLST group (3.6% (95% CI, 2.62% to 4.83%)) and in the PLCOm2012 high-risk group (14.3% (95% CI, 6.8% to 27.7%)) than in the PLCOm2012 low-risk group (3.7% (95% CI, 2.9% to 4.8%)) (p=0.016). Detection rate of lung cancer was also significantly higher (p=0.018) among PLCOm2012 high-risk patients (5.7% (95% CI, 2.5% to 12.6%)) than in the PLCOm2012 low-risk individuals (0.2% (95% CI, 0.1% to 1.1%)). The false-positive rate for NLST criteria (16.4% (95% CI, 13.2% to 20.1%)) was higher (p<0.001) than for PLCOm2012 criteria (7.6 (95% CI, 5.3% to 10.5%)). Discussion Our study indicates a lower performance when screening low-risk individuals in comparison to screening patients meeting NLST criteria and PLCOm2012 high-risk patients. Also, incorporating PLCOm2012 6-year lung cancer risk ≥0.0151 as an eligibility criterion seems to increase lung cancer screening effectiveness.
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Affiliation(s)
| | | | | | | | | | - Gilberto Szarf
- Department of Radiology, Hospital Israelita Albert Einstein, São Paulo, Brazil
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Paz-Elizur T, Leitner-Dagan Y, Meyer KB, Markus B, Giorgi FM, O’Reilly M, Kim H, Evgy Y, Fluss R, Freedman LS, Rintoul RC, Ponder B, Livneh Z. DNA Repair Biomarker for Lung Cancer Risk and its Correlation With Airway Cells Gene Expression. JNCI Cancer Spectr 2020; 4:pkz067. [PMID: 32064457 PMCID: PMC7012022 DOI: 10.1093/jncics/pkz067] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 07/23/2019] [Accepted: 08/20/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Improving lung cancer risk assessment is required because current early-detection screening criteria miss most cases. We therefore examined the utility for lung cancer risk assessment of a DNA Repair score obtained from OGG1, MPG, and APE1 blood tests. In addition, we examined the relationship between the level of DNA repair and global gene expression. METHODS We conducted a blinded case-control study with 150 non-small cell lung cancer case patients and 143 control individuals. DNA Repair activity was measured in peripheral blood mononuclear cells, and the transcriptome of nasal and bronchial cells was determined by RNA sequencing. A combined DNA Repair score was formed using logistic regression, and its correlation with disease was assessed using cross-validation; correlation of expression to DNA Repair was analyzed using Gene Ontology enrichment. RESULTS DNA Repair score was lower in case patients than in control individuals, regardless of the case's disease stage. Individuals at the lowest tertile of DNA Repair score had an increased risk of lung cancer compared to individuals at the highest tertile, with an odds ratio (OR) of 7.2 (95% confidence interval [CI] = 3.0 to 17.5; P < .001), and independent of smoking. Receiver operating characteristic analysis yielded an area under the curve of 0.89 (95% CI = 0.82 to 0.93). Remarkably, low DNA Repair score correlated with a broad upregulation of gene expression of immune pathways in patients but not in control individuals. CONCLUSIONS The DNA Repair score, previously shown to be a lung cancer risk factor in the Israeli population, was validated in this independent study as a mechanism-based cancer risk biomarker and can substantially improve current lung cancer risk prediction, assisting prevention and early detection by computed tomography scanning.
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Affiliation(s)
- Tamar Paz-Elizur
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Yael Leitner-Dagan
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Kerstin B Meyer
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Barak Markus
- Bioinformatics Unit, Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Federico M Giorgi
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Martin O’Reilly
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Hyunjin Kim
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Yentl Evgy
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Ronen Fluss
- Biostatistics Unit, Gertner Institute for Epidemiology and Public Health Policy Sheba Medical Center, Tel Hashomer, Israel
| | - Laurence S Freedman
- Biostatistics Unit, Gertner Institute for Epidemiology and Public Health Policy Sheba Medical Center, Tel Hashomer, Israel
| | - Robert C Rintoul
- Department of Oncology, University of Cambridge, Cambridge, UK
- Department of Thoracic Oncology, Royal Papworth Hospital, Cambridge, UK
| | - Bruce Ponder
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Zvi Livneh
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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Wood DE, Kazerooni EA, Baum SL, Eapen GA, Ettinger DS, Hou L, Jackman DM, Klippenstein D, Kumar R, Lackner RP, Leard LE, Lennes IT, Leung ANC, Makani SS, Massion PP, Mazzone P, Merritt RE, Meyers BF, Midthun DE, Pipavath S, Pratt C, Reddy C, Reid ME, Rotter AJ, Sachs PB, Schabath MB, Schiebler ML, Tong BC, Travis WD, Wei B, Yang SC, Gregory KM, Hughes M. Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2019; 16:412-441. [PMID: 29632061 DOI: 10.6004/jnccn.2018.0020] [Citation(s) in RCA: 383] [Impact Index Per Article: 76.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.
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Whittaker Brown SA, Padilla M, Mhango G, Powell C, Salvatore M, Henschke C, Yankelevitz D, Sigel K, de-Torres JP, Wisnivesky J. Interstitial Lung Abnormalities and Lung Cancer Risk in the National Lung Screening Trial. Chest 2019; 156:1195-1203. [PMID: 31404527 DOI: 10.1016/j.chest.2019.06.041] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 06/11/2019] [Accepted: 06/25/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Some interstitial lung diseases are associated with lung cancer. However, it is unclear whether asymptomatic interstitial lung abnormalities convey an independent risk. OBJECTIVES The goal of this study was to assess whether interstitial lung abnormalities are associated with an increased risk of lung cancer. METHODS Data from all participants in the National Lung Cancer Trial were analyzed, except for subjects with preexisting interstitial lung disease or prevalent lung cancers. The primary analysis included those who underwent low-dose CT imaging; those undergoing chest radiography were included in a confirmatory analysis. Participants with evidence of reticular/reticulonodular opacities, honeycombing, fibrosis, or scarring were classified as having interstitial lung abnormalities. Lung cancer incidence and mortality in participants with and without interstitial lung abnormalities were compared by using Poisson and Cox regression, respectively. RESULTS Of the 25,041 participants undergoing low-dose CT imaging included in the primary analysis, 20.2% had interstitial lung abnormalities. Participants with interstitial lung abnormalities had a higher incidence of lung cancer (incidence rate ratio, 1.61; 95% CI, 1.30-1.99). Interstitial lung abnormalities were associated with higher lung cancer incidence on adjusted analyses (incidence rate ratio, 1.33; 95% CI, 1.07-1.65). Lung cancer-specific mortality was also greater in participants with interstitial lung abnormalities. Similar findings were obtained in the analysis of participants undergoing chest radiography. CONCLUSIONS Asymptomatic interstitial lung abnormalities are an independent risk factor for lung cancer that can be incorporated into risk score models.
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Affiliation(s)
- Stacey-Ann Whittaker Brown
- Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
| | - Maria Padilla
- Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Grace Mhango
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Charles Powell
- Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Mary Salvatore
- Division of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Claudia Henschke
- Division of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - David Yankelevitz
- Division of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Keith Sigel
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Juan P de-Torres
- Division of Respiratory Medicine, Clínica Universidad de Navarra, Pamplona, Spain
| | - Juan Wisnivesky
- Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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Balata H, Evison M, Sharman A, Crosbie P, Booton R. CT screening for lung cancer: Are we ready to implement in Europe? Lung Cancer 2019; 134:25-33. [PMID: 31319989 DOI: 10.1016/j.lungcan.2019.05.028] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/12/2019] [Accepted: 05/26/2019] [Indexed: 12/23/2022]
Abstract
Lung cancer screening with low-dose CT (LDCT) is already available in certain parts of the world, such as the United States, but not yet in Europe. The recently published European position statement on lung cancer screening has recommended planning for implementation of screening to start within 18-months [1]. Pilot European programmes are already underway, primarily in the United Kingdom (UK), delivering lung cancer screening to their local populations. This review article acknowledges the evidence base for LDCT screening and will discuss the challenges that still need to be overcome in an attempt to answer the question: are we ready to implement in Europe?
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Affiliation(s)
- Haval Balata
- Manchester Thoracic Oncology Centre (MTOC), North West Lung Centre, Manchester University NHS Foundation Trust, Southmoor Road, Wythenshawe, UK; Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester, UK.
| | - Matthew Evison
- Manchester Thoracic Oncology Centre (MTOC), North West Lung Centre, Manchester University NHS Foundation Trust, Southmoor Road, Wythenshawe, UK
| | - Anna Sharman
- Manchester Thoracic Oncology Centre (MTOC), North West Lung Centre, Manchester University NHS Foundation Trust, Southmoor Road, Wythenshawe, UK
| | - Philip Crosbie
- Manchester Thoracic Oncology Centre (MTOC), North West Lung Centre, Manchester University NHS Foundation Trust, Southmoor Road, Wythenshawe, UK; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Richard Booton
- Manchester Thoracic Oncology Centre (MTOC), North West Lung Centre, Manchester University NHS Foundation Trust, Southmoor Road, Wythenshawe, UK
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49
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Cheng YI, Davies MPA, Liu D, Li W, Field JK. Implementation planning for lung cancer screening in China. PRECISION CLINICAL MEDICINE 2019; 2:13-44. [PMID: 35694700 PMCID: PMC8985785 DOI: 10.1093/pcmedi/pbz002] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/19/2018] [Accepted: 12/24/2018] [Indexed: 02/05/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in China, with over 690 000 lung cancer deaths estimated in 2018. The mortality has increased about five-fold from the mid-1970s to the 2000s. Lung cancer low-dose computerized tomography (LDCT) screening in smokers was shown to improve survival in the US National Lung Screening Trial, and more recently in the European NELSON trial. However, although the predominant risk factor, smoking contributes to a lower fraction of lung cancers in China than in the UK and USA. Therefore, it is necessary to establish Chinese-specific screening strategies. There have been 23 associated programmes completed or still ongoing in China since the 1980s, mainly after 2000; and one has recently been planned. Generally, their entry criteria are not smoking-stringent. Most of the Chinese programmes have reported preliminary results only, which demonstrated a different high-risk subpopulation of lung cancer in China. Evidence concerning LDCT screening implementation is based on results of randomized controlled trials outside China. LDCT screening programmes combining tobacco control would produce more benefits. Population recruitment (e.g. risk-based selection), screening protocol, nodule management and cost-effectiveness are discussed in detail. In China, the high-risk subpopulation eligible for lung cancer screening has not as yet been confirmed, as all the risk parameters have not as yet been determined. Although evidence on best practice for implementation of lung cancer screening has been accumulating in other countries, further research in China is urgently required, as China is now facing a lung cancer epidemic.
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Affiliation(s)
- Yue I Cheng
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
| | - Michael P A Davies
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
| | - Dan Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - John K Field
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
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Tammemägi MC, ten Haaf K, Toumazis I, Kong CY, Han SS, Jeon J, Commins J, Riley T, Meza R. Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial. JAMA Netw Open 2019; 2:e190204. [PMID: 30821827 PMCID: PMC6484623 DOI: 10.1001/jamanetworkopen.2019.0204] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals. OBJECTIVES To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results. DESIGN, SETTING, AND PARTICIPANTS A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n = 14 576) and ACRIN (n = 7653) participants who had 3 screens, adequate follow-up, and complete predictor information. MAIN OUTCOMES AND MEASURES Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results. RESULTS Overall, the mean (SD) age of 22 229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22 229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P < .001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative. CONCLUSIONS AND RELEVANCE According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT00047385.
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Affiliation(s)
- Martin C. Tammemägi
- Department of Health Sciences, Brock University, St Catharines, Ontario, Canada
| | - Kevin ten Haaf
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Iakovos Toumazis
- Department of Radiology, Stanford University School of Medicine, Palo Alto, California
| | - Chung Yin Kong
- Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Summer S. Han
- Department of Medicine, Stanford University School of Medicine, Palo Alto, California
| | - Jihyoun Jeon
- School of Public Health, University of Michigan, Ann Arbor
| | - John Commins
- Information Management Systems, Rockville, Maryland
| | - Thomas Riley
- Information Management Systems, Rockville, Maryland
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor
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