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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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Saunders CL. Using Routine Data to Improve Lesbian, Gay, Bisexual, and Transgender Health. Interact J Med Res 2024; 13:e53311. [PMID: 38691398 PMCID: PMC11097049 DOI: 10.2196/53311] [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: 10/03/2023] [Revised: 02/28/2024] [Accepted: 03/26/2024] [Indexed: 05/03/2024] Open
Abstract
The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced-descriptive epidemiology, risk prediction, intervention development, and impact evaluation-and are discussed as frameworks for translating data into research with the potential to improve health.
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Nguyen OTD, Fotopoulos I, Markaki M, Tsamardinos I, Lagani V, Røe OD. Improving Lung Cancer Screening Selection: The HUNT Lung Cancer Risk Model for Ever-Smokers Versus the NELSON and 2021 United States Preventive Services Task Force Criteria in the Cohort of Norway: A Population-Based Prospective Study. JTO Clin Res Rep 2024; 5:100660. [PMID: 38586302 PMCID: PMC10998221 DOI: 10.1016/j.jtocrr.2024.100660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 02/14/2024] [Accepted: 03/03/2024] [Indexed: 04/09/2024] Open
Abstract
Background Improving the method for selecting participants for lung cancer (LC) screening is an urgent need. Here, we compared the performance of the Helseundersøkelsen i Nord-Trøndelag (HUNT) Lung Cancer Model (HUNT LCM) versus the Dutch-Belgian lung cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON)) and 2021 United States Preventive Services Task Force (USPSTF) criteria regarding LC risk prediction and efficiency. Methods We used linked data from 10 Norwegian prospective population-based cohorts, Cohort of Norway. The study included 44,831 ever-smokers, of which 686 (1.5%) patients developed LC; the median follow-up time was 11.6 years (0.01-20.8 years). Results Within 6 years, 222 (0.5%) individuals developed LC. The NELSON and 2021 USPSTF criteria predicted 37.4% and 59.5% of the LC cases, respectively. By considering the same number of individuals as the NELSON and 2021 USPSTF criteria selected, the HUNT LCM increased the LC prediction rate by 41.0% and 12.1%, respectively. The HUNT LCM significantly increased sensitivity (p < 0.001 and p = 0.028), and reduced the number needed to predict one LC case (29 versus 40, p < 0.001 and 36 versus 40, p = 0.02), respectively. Applying the HUNT LCM 6-year 0.98% risk score as a cutoff (14.0% of ever-smokers) predicted 70.7% of all LC, increasing LC prediction rate with 89.2% and 18.9% versus the NELSON and 2021 USPSTF, respectively (both p < 0.001). Conclusions The HUNT LCM was significantly more efficient than the NELSON and 2021 USPSTF criteria, improving the prediction of LC diagnosis, and may be used as a validated clinical tool for screening selection.
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Affiliation(s)
- Olav Toai Duc Nguyen
- Department of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Cancer Clinic, Levanger, Norway
| | - Ioannis Fotopoulos
- Department of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece
| | - Maria Markaki
- Department of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece
- Institute of Applied and Computational Mathematics, Heraklion, Greece
- JADBio Gnosis Data Analysis (DA) S.A., Science and Technology Park of Crete (STEP-C), Heraklion, Greece
| | - Vincenzo Lagani
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Saudi Data and Artificial Intelligence Authority (SDAIA)–KAUST Center of Excellence in Data Science and Artificial Intelligence, Thuwal, Saudi Arabia
- Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia
| | - Oluf Dimitri Røe
- Department of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Cancer Clinic, Levanger, Norway
- Clinical Cancer Research Center and Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
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Ten Haaf K. Considerations for Enhancing Lung Cancer Risk Prediction and Screening in Asian Populations. J Thorac Oncol 2024; 19:373-375. [PMID: 38453324 DOI: 10.1016/j.jtho.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 03/09/2024]
Affiliation(s)
- Kevin Ten Haaf
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.
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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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Resong PJ, Niu J, Duhon GF, Foxhall LE, Shete S, Volk RJ, Toumazis I. Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers. Cancer Prev Res (Phila) 2024; 17:51-57. [PMID: 38212272 PMCID: PMC10926168 DOI: 10.1158/1940-6207.capr-23-0359] [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: 08/29/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024]
Abstract
Current lung cancer screening (LCS) guidelines rely on age and smoking history. Despite its benefit, only 5%-15% of eligible patients receive LCS. Personalized screening strategies select individuals based on their lung cancer risk and may increase LCS's effectiveness. We assess current LCS practices and the acceptability of personalized LCS among primary care providers (PCP) in Texas. We surveyed 32,983 Texas-based PCPs on an existing network (Protocol 2019-1257; PI: Dr. Shete) and 300 attendees of the 2022 Texas Academy of Family Physicians (TAFP) conference. We analyzed the responses by subgroups of interest. Using nonparametric bootstrap, we derived an enriched dataset to develop logistic regression models to understand current LCS practices and acceptability of personalized LCS. Response rates were 0.3% (n = 91) and 15% (n = 60) for the 2019-1257 and TAFP surveys, respectively. Most (84%) respondents regularly assess LCS in their practice. Half of the respondents were interested in adopting personalized LCS. The majority (66%) of respondents expressed concerns regarding time availability with the personalized LCS. Most respondents would use biomarkers as an adjunct to assess eligibility (58%), or to help guide indeterminate clinical findings (63%). There is a need to enhance the engagement of Texas-based PCPs in LCS. Most of the respondents expressed interest in personalized LCS. Time availability was the main concern related to personalized LCS. Findings from this project highlight the need for better education of Texas-based PCPs on the benefits of LCS, and the development of efficient decision tools to ensure successful implementation of personalized LCS. PREVENTION RELEVANCE Personalized LCS facilitated by a risk model and/or a biomarker test is proposed as an alternative to existing programs. Acceptability of personalized approach among PCPs is unknown. The goal of this study is to assess the acceptability of personalized LCS among PCPs.
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Affiliation(s)
- Paul J Resong
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- University of Nevada, Reno School of Medicine
| | - Jiangong Niu
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gabrielle F Duhon
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lewis E Foxhall
- Department of Clinical Cancer Prevention, Division of OVP, Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sanjay Shete
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Robert J Volk
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Iakovos Toumazis
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Li M, Cao SM, Dimou N, Wu L, Li JB, Yang J. Association of Metabolic Syndrome With Risk of Lung Cancer: A Population-Based Prospective Cohort Study. Chest 2024; 165:213-223. [PMID: 37572975 PMCID: PMC10790176 DOI: 10.1016/j.chest.2023.08.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/30/2023] [Accepted: 08/02/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Both the incidence of lung cancer and the prevalence of metabolic syndrome (MetS) have been increasing worldwide. The relationship between MetS and lung cancer remains controversial. RESEARCH QUESTION What is the risk of lung cancer associated with MetS and its components? STUDY DESIGN AND METHODS Multivariable Cox regression models were used to estimate the hazard ratio (HR) of MetS-related variables on lung cancer risk, both overall and by histologic subtype, in the UK Biobank. Stratified analyses were conducted by sex, tobacco use status, and use of medication. HR curves were used to test the nonlinear associations between the metabolic markers and the risk of lung cancer. RESULTS Of the 331,877 participants included in this study, a total of 77,173 participants had a diagnosis of MetS at enrollment. During a median follow-up of 10.9 years, lung cancer as the primary site developed in 2,425 participants. The HRs of MetS were 1.21 (95% CI, 1.09-1.33), 1.28 (95% CI, 1.10-1.50), and 1.16 (95% CI, 0.94-1.44) for the overall risk of lung cancer, adenocarcinoma, and squamous cell carcinoma, respectively. The HRs increased with the number of metabolic abnormalities from 1.11 to approximately 1.4 or 1.5 for those with one to five disorders. Positive association with lung cancer was observed for low high-density lipoprotein cholesterol (HDL-C), elevated waist circumference, and hyperglycemia. The relationship between MetS and lung cancer was modified by sex, with a stronger effect in female patients (P = .031). The risk of lung cancer resulting from MetS was elevated mainly among individuals who used tobacco, although the modification effect of tobacco use was not statistically significant. A nonlinear association was found between lung cancer and HDL-C, waist circumference, and glycated hemoglobin. INTERPRETATION The increased risk of lung cancer associated with MetS suggests the importance of taking metabolic status and markers into consideration for the primary prevention of lung cancer and the selection of high-risk populations for lung cancer screening.
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Affiliation(s)
- Mengmeng Li
- Department of Cancer Prevention, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China.
| | - Su-Mei Cao
- Department of Cancer Prevention, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Lan Wu
- Department of Cancer Prevention, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ji-Bin Li
- Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun Yang
- School of Public Health, Guangzhou Medical University, Guangzhou, 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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Callender T, Imrie F, Cebere B, Pashayan N, Navani N, van der Schaar M, Janes SM. Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study. PLoS Med 2023; 20:e1004287. [PMID: 37788223 PMCID: PMC10547178 DOI: 10.1371/journal.pmed.1004287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/29/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. METHODS AND FINDINGS For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. CONCLUSIONS We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.
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Affiliation(s)
- Thomas Callender
- Department of Respiratory Medicine, University College London, London, United Kingdom
| | - Fergus Imrie
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California, United States of America
| | - Bogdan Cebere
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London, United Kingdom
| | - Neal Navani
- Department of Respiratory Medicine, University College London, London, United Kingdom
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, United Kingdom
- Alan Turing Institute, London, United Kingdom
| | - Sam M. Janes
- Department of Respiratory Medicine, University College London, London, United Kingdom
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Harrison H, Wood A, Pennells L, Rossi SH, Callister M, Cartledge J, Stewart GD, Usher-Smith JA. Estimating the Effectiveness of Kidney Cancer Screening Within Lung Cancer Screening Programmes: A Validation in UK Biobank. Eur Urol Oncol 2023; 6:351-353. [PMID: 37003861 DOI: 10.1016/j.euo.2023.02.012] [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: 10/06/2022] [Revised: 02/07/2023] [Accepted: 02/23/2023] [Indexed: 04/03/2023]
Abstract
In the absence of population-based screening, addition of screening for kidney cancer to lung cancer screening could provide an efficient and low-resource means to improve early detection. In this study, we used the UK Biobank cohort (n = 442 865) to determine the performance of the Yorkshire Lung Cancer Screening Trial (YLST) eligibility criteria for selecting individuals for kidney cancer screening. We measured the performance of two models widely used to determine eligibility for lung cancer screening (PLCO[m2012] and the Liverpool-Lung-Project-v2) and the performance of the combined YLST criteria. We found that the lung cancer models have discrimination (area under the receiver operating curve) between 0.60 and 0.68 for kidney cancer. In the UK, one in four cases (25%) of kidney cancer cases is expected to occur in those eligible for lung cancer screening, and one case of kidney cancer detected for every 200 people invited to lung cancer screening. These results suggest that adding kidney cancer screening to lung cancer screening would be an effective strategy to improve early detection rates of kidney cancer. However, most kidney cancers would not be picked up by this approach. This analysis does not address other important considerations about kidney cancer screening, such as overdiagnosis. PATIENT SUMMARY: It has been proposed that adding-on kidney cancer screening to lung cancer screening (both carried out by a computed tomography scan of the chest/abdomen) would be an easy and low-cost way of detecting cases of kidney cancer earlier, when these can be treated more easily. Lung cancer screening is usually targeted at people who are at a high risk (eg, older smokers); therefore, here we look at whether the same group of people are also at a high risk of kidney cancer. Our analysis shows that one in four people later diagnosed with kidney cancer are also at a high risk of lung cancer; hence, a combined screening programme could detect up to a quarter of kidney cancers.
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Affiliation(s)
- Hannah Harrison
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Angela Wood
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Lisa Pennells
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sabrina H Rossi
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Matthew Callister
- Department of Respiratory Medicine, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Jon Cartledge
- St James University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Juliet A Usher-Smith
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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11
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Tian W, Zhu G, Xiao W, Gao B, Lu W, Wang Y. Stroke burden and attributable risk factors in China, 1990-2019. Front Neurol 2023; 14:1193056. [PMID: 37292127 PMCID: PMC10245554 DOI: 10.3389/fneur.2023.1193056] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/03/2023] [Indexed: 06/10/2023] Open
Abstract
Background and purpose Understanding the temporal trends of stroke burden and its attributable risk factors are essential for targeted prevention strategies. We aimed to describe the temporal trends and attributable risk factors of stroke in China. Methods Data on the stroke burden [incidence, prevalence, mortality, and disability-adjusted life years (DALYs)] and the population-attributable fraction for stroke risk factors from 1990 to 2019 were obtained from the Global Burden of Disease Study 2019 (GBD 2019). We analyzed trends in the burden of stroke and its attributable risk factors from 1990 to 2019, and the characteristics of stroke-attributable risk factors by sex, age group, and stroke subtype. Results From 1990 to 2019, the age-standardized incidence, mortality, and DALY rates for total stroke decreased by 9.3% (3.3, 15.5), 39.8% (28.6, 50.7), and 41.6% (30.7, 50.9) respectively. The corresponding indicators all decreased for intracerebral hemorrhage and subarachnoid hemorrhage. The age-standardized incidence rate of ischemic stroke increased by 39.5% (33.5 to 46.2) for male patients and by 31.4% (24.7 to 37.7) for female patients, and the age-standardized mortality and DALY rates remained almost unchanged. The three leading stroke risk factors were high systolic blood pressure, ambient particulate matter pollution, and smoking. High systolic blood pressure has remained the leading risk factor since 1990. The attributable risk of ambient particulate matter pollution shows a clear upward trend. Smoking and alcohol consumption were important risk factors for men. Conclusion This study reinforced the findings of an increased stroke burden in China. Precise stroke prevention strategies are needed to reduce the disease burden of stroke.
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Affiliation(s)
- Wenxin Tian
- School of Public Health, Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
| | - Guanghan Zhu
- School of Public Health, Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
| | - Wenbo Xiao
- School of Public Health, Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
| | - Bei Gao
- School of Public Health, Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
| | - Wenli Lu
- School of Public Health, Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
| | - Yuan Wang
- School of Public Health, Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin Medical University, Heping District, Tianjin, China
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12
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Dennison RA, Taylor LC, Morris S, Boscott RA, Harrison H, Moorthie SA, Rossi SH, Stewart GD, Usher-Smith JA. Public Preferences for Determining Eligibility for Screening in Risk-Stratified Cancer Screening Programs: A Discrete Choice Experiment. Med Decis Making 2023; 43:374-386. [PMID: 36786399 PMCID: PMC10021112 DOI: 10.1177/0272989x231155790] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
BACKGROUND Risk stratification has been proposed to improve the efficiency of population-level cancer screening. We aimed to describe and quantify the relative importance of different attributes of potential screening programs among the public, focusing on stratifying eligibility. METHODS We conducted a discrete choice experiment in which respondents selected between 2 hypothetical screening programs in a series of 9 questions. We presented the risk factors used to determine eligibility (age, sex, or lifestyle or genetic risk scores) and anticipated outcomes based on eligibility criteria with different sensitivity and specificity levels. We performed conditional logit regression models and used the results to estimate preferences for different approaches. We also analyzed free-text comments on respondents' views on the programs. RESULTS A total of 1,172 respondents completed the survey. Sensitivity was the most important attribute (7 and 11 times more important than specificity and risk factors, respectively). Eligibility criteria based on age and sex or genetics were preferred over age alone and lifestyle risk scores. Phenotypic and polygenic risk prediction models would be more acceptable than screening everyone aged 55 to 70 y if they had high discrimination (area under the receiver-operating characteristic curve ≥0.75 and 0.80, respectively). LIMITATIONS Although our sample was representative with respect to age, sex, and ethnicity, it may not be representative of the UK population regarding other important characteristics. Also, some respondents may have not understood all the information provided to inform decision making. CONCLUSIONS The public prioritized lives saved from cancer over reductions in numbers screened or experiencing unnecessary follow-up. Incorporating personal-level risk factors into screening eligibility criteria is acceptable to the public if it increases sensitivity; therefore, maximizing sensitivity in model development and communication could increase uptake. HIGHLIGHTS The public prioritized lives saved when considering changing from age-based eligibility criteria to risk-stratified cancer screening over reductions in numbers of people being screened or experiencing unnecessary follow-up.The risk stratification strategy used to do this was the least important component, although age plus sex or genetics were relatively preferable to using age alone and lifestyle risk scores.Communication strategies that emphasize improvements in the numbers of cancers detected or not missed across the population are more likely to be salient than reductions in unnecessary investigations or follow-up among some groups.Future research should focus on developing implementation strategies that maximize gains in sensitivity within the context of resource constraints and how to present attributes relating to specificity to facilitate understanding and informed decision making.
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Affiliation(s)
- Rebecca A Dennison
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Lily C Taylor
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Stephen Morris
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Rachel A Boscott
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Hannah Harrison
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Sabrina H Rossi
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Juliet A Usher-Smith
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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13
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Liao W, Coupland CAC, Burchardt J, Baldwin DR, Gleeson FV, Hippisley-Cox J. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models. THE LANCET RESPIRATORY MEDICINE 2023:S2213-2600(23)00050-4. [PMID: 37030308 DOI: 10.1016/s2213-2600(23)00050-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 04/08/2023]
Abstract
BACKGROUND Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. METHODS For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R2D]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP]v2, LLPv3, Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO]M2012, PLCOM2014, Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. FINDINGS There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R2D in both sexes in the QResearch validation cohort and 59% of the R2D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLPv2 and PLCOM2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. INTERPRETATION The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. FUNDING Innovate UK (UK Research and Innovation). TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Weiqi Liao
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Carol A C Coupland
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; School of Medicine, University of Nottingham, Nottingham, UK
| | - Judith Burchardt
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David R Baldwin
- School of Medicine, University of Nottingham, Nottingham, UK; Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
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Jantzen R, Ezer N, Camilleri-Broët S, Tammemägi MC, Broët P. Evaluation of the accuracy of the PLCO m2012 6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort. CMAJ Open 2023; 11:E314-E322. [PMID: 37041013 PMCID: PMC10095260 DOI: 10.9778/cmajo.20210335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND The PLCOm2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCOm2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies. METHODS We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCOm2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCOm2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively. RESULTS Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCOm2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCOm2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCOm2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%). INTERPRETATION In a cohort of Quebec smokers, the PLCOm2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.
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Affiliation(s)
- Rodolphe Jantzen
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Nicole Ezer
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Sophie Camilleri-Broët
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Martin C Tammemägi
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Philippe Broët
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
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15
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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: 103] [Impact Index Per Article: 103.0] [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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Pan Z, Zhang R, Shen S, Lin Y, Zhang L, Wang X, Ye Q, Wang X, Chen J, Zhao Y, Christiani DC, Li Y, Chen F, Wei Y. OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations. EBioMedicine 2023; 88:104443. [PMID: 36701900 PMCID: PMC9881220 DOI: 10.1016/j.ebiom.2023.104443] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 12/27/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND A reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model. METHODS We developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCOm2012), PLCO modified 2014 (PLCOall2014), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RIEO), and illustrated the clinical utility by the decision curve analysis. FINDINGS For general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCOall2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCOall2014 (AUCPLCOall2014-AUCOWL < 1%). For ever-smokers, OWL outperformed PLCOm2012 and PLCOall2014 among ever-smokers in validation set 1 (AUCOWL = 0.842, 95% CI: 0.814-0.871; AUCPLCOm2012 = 0.792, 95% CI: 0.760-0.823; AUCPLCOall2014 = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCOm2012 and PLCOall2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCOm2012, PLCOall2014 (RIEO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RIEO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCOall2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCOm2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCOm2012 and PLCOall2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations. INTERPRETATION OWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer. FUNDING National Natural Science Foundation of China, the US NIH.
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Affiliation(s)
- Zoucheng Pan
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Ruyang Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Sipeng Shen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yunzhi Lin
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Longyao Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xiang Wang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Qian Ye
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xuan Wang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jiajin Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yang Zhao
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA; Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Yongyue Wei
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Xueyuan Road, Haidian District, Beijing 100191, China.
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17
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Cressman S, Weber MF, Ngo PJ, Wade S, Behar Harpaz S, Caruana M, Tremblay A, Manser R, Stone E, Atkar-Khattra S, Karikios D, Ho C, Fernandes A, Yi Weng J, McWilliams A, Myers R, Mayo J, Yee J, Yuan R, Marshall HM, Fong KM, Lam S, Canfell K, Tammemägi MC. Economic impact of using risk models for eligibility selection to the International lung screening Trial. Lung Cancer 2023; 176:38-45. [PMID: 36592498 DOI: 10.1016/j.lungcan.2022.12.011] [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: 09/05/2022] [Revised: 12/05/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Using risk models as eligibility criteria for lung screening can reduce race and sex-based disparities. We used data from the International Lung Screening Trial(ILST; NCT02871856) to compare the economic impact of using the PLCOm2012 risk model or the US Preventative Services' categorical age-smoking history-based criteria (USPSTF-2013). MATERIALS AND METHODS The cost-effectiveness of using PLCOm2012 versus USPSTF-2013 was evaluated with a decision analytic model based on the ILST and other screening trials. The primary outcomes were costs in 2020 International Dollars ($), quality-adjusted life-years (QALY) and incremental net benefit (INB, in $ per QALY). Secondary outcomes were selection characteristics and cancer detection rates (CDR). RESULTS Compared with the USPSTF-2013 criteria, the PLCOm2012 risk model resulted in $355 of cost savings per 0.2 QALYs gained (INB=$4294 at a willingness-to-pay threshold of $20 000/QALY (95 %CI: $4205-$4383). Using the risk model was more cost-effective in females at both a 1.5 % and 1.7 % 6-year risk threshold (INB=$6616 and $6112, respectively), compared with males ($5221 and $695). The PLCOm2012 model selected more females, more individuals with fewer years of formal education, and more people with other respiratory illnesses in the ILST. The CDR with the risk model was higher in females compared with the USPSTF-2013 criteria (Risk Ratio = 7.67, 95 % CI: 1.87-31.38). CONCLUSION The PLCOm2012 model saved costs, increased QALYs and mitigated socioeconomic and sex-based disparities in access to screening.
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Affiliation(s)
- Sonya Cressman
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada; The Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada.
| | - Marianne F Weber
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney 2011, Australia
| | - Preston J Ngo
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney 2011, Australia
| | - Stephen Wade
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney 2011, Australia
| | - Silvia Behar Harpaz
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney 2011, Australia
| | - Michael Caruana
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney 2011, Australia
| | - Alain Tremblay
- Division of Respiratory Medicine and Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Renee Manser
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parksville, Victoria, 3050, Australia; Department of Internal Medicine, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia; University of Melbourne, Department of. Medicine, Royal Melbourne Hospital, Parksville, Victoria, 3010, Australia
| | - Emily Stone
- Department of Thoracic Medicine and Lung Transplantation, St Vincent Hospital, Sydney, Australia; School of Clinical Medicine; School of Public Health, University of Sydney, Australia
| | | | - Deme Karikios
- Nepean Clinical School, The University of Sydney, NSW 2747, Australia
| | - Cheryl Ho
- BC Cancer, Vancouver, British Columbia, Australia; Faculty of Medicine, University of British Columbia, Vancouver, British Columbia
| | - Aleisha Fernandes
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada; Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Jing Yi Weng
- Department of Primary Care and Population Health, University College London, London, United Kingdom
| | - Annette McWilliams
- Department of Respiratory Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Renelle Myers
- BC Cancer Research Institute, Vancouver, BC, Canada; BC Cancer, Vancouver, British Columbia, Australia; Faculty of Medicine, University of British Columbia, Vancouver, British Columbia
| | - John Mayo
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia
| | - John Yee
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia
| | - Ren Yuan
- BC Cancer, Vancouver, British Columbia, Australia; Faculty of Medicine, University of British Columbia, Vancouver, British Columbia
| | - Henry M Marshall
- The Prince Charles Hospital and University of Queensland Thoracic Research Centre, Brisbane, QLD, Australia
| | - Kwun M Fong
- The Prince Charles Hospital and University of Queensland Thoracic Research Centre, Brisbane, QLD, Australia
| | - Stephen Lam
- BC Cancer Research Institute, Vancouver, BC, Canada; BC Cancer, Vancouver, British Columbia, Australia; Faculty of Medicine, University of British Columbia, Vancouver, British Columbia
| | - Karen Canfell
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney 2011, Australia
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18
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[China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2023 Version)]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2023; 26:1-9. [PMID: 36792074 PMCID: PMC9987116 DOI: 10.3779/j.issn.1009-3419.2023.102.10] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Indexed: 02/17/2023]
Abstract
Lung cancer is the leading cause of cancer-related death in China. The effectiveness of low-dose computed tomography (LDCT) screening has been further validated in recent years, and significant progress has been made in research on identifying high-risk individuals, personalizing screening interval, and management of screen-detected findings. The aim of this study is to revise China national lung cancer screening guideline with LDCT (2018 version). The China Lung Cancer Early Detection and Treatment Expert Group (CLCEDTEG) designated by the China's National Health Commission, and China Lung Oncolgy Group experts, jointly participated in the revision of Chinese lung cancer screening guideline (2023 version). This revision is based on the recent advances in LDCT lung cancer screening at home and abroad, and the epidemiology of lung cancer in China. The following aspects of the guideline were revised: (1) lung cancer risk factors besides smoking were considered for the identification of high risk population; (2) LDCT scan parameters were further classified; (3) longer screening interval is recommended for individuals who had negative LDCT screening results for two consecutive rounds; (4) the follow-up interval for positive nodules was extended from 3 months to 6 months; (5) the role of multi-disciplinary treatment (MDT) in the management of positive nodules, diagnosis and treatment of lung cancer were emphasized. This revision clarifies the screening, intervention and treatment pathways, making the LDCT screening guideline more appropriate for China. Future researches based on emerging technologies, including biomarkers and artificial intelligence, are needed to optimize LDCT screening in China in the future.
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19
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Robbins HA, Alcala K, Moez EK, Guida F, Thomas S, Zahed H, Warkentin MT, Smith-Byrne K, Brhane Y, Muller D, Feng X, Albanes D, Aldrich MC, Arslan AA, Bassett J, Berg CD, Cai Q, Chen C, Davies MPA, Diergaarde B, Field JK, Freedman ND, Huang WY, Johansson M, Jones M, Koh WP, Lam S, Lan Q, Langhammer A, Liao LM, Liu G, Malekzadeh R, Milne RL, Montuenga LM, Rohan T, Sesso HD, Severi G, Sheikh M, Sinha R, Shu XO, Stevens VL, Tammemägi MC, Tinker LF, Visvanathan K, Wang Y, Wang R, Weinstein SJ, White E, Wilson D, Yuan JM, Zhang X, Zheng W, Amos CI, Brennan P, Johansson M, Hung RJ. Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program. Ann Epidemiol 2023; 77:1-12. [PMID: 36404465 PMCID: PMC9835888 DOI: 10.1016/j.annepidem.2022.10.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 10/23/2022] [Accepted: 10/24/2022] [Indexed: 01/21/2023]
Abstract
The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize low-dose CT (LDCT) lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1161 proteins in a nested-case control study within 2 prospective cohorts (n = 252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n = 479 cases and 479 controls). Eligible participants had a current or former history of smoking and cases were diagnosed up to 3 years following blood draw. The Nodule Malignancy project measured 1078 proteins among participants with a heavy smoking history within four LDCT screening studies (n = 425 cases diagnosed up to 5 years following blood draw, 430 benign-nodule controls, and 398 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n = 1696 cases and 2926 subcohort representatives), and in the Nodule Malignancy project within five LDCT screening studies (n = 675 cases, 680 benign-nodule controls, and 648 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.
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Affiliation(s)
- Hilary A Robbins
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
| | - Karine Alcala
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Elham Khodayari Moez
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
| | - Florence Guida
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Sera Thomas
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
| | - Hana Zahed
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Matthew T Warkentin
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | | | - Yonathan Brhane
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
| | - David Muller
- Division of Genetic Medicine, Imperial College London School of Public Health, London, UK
| | - Xiaoshuang Feng
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Melinda C Aldrich
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Alan A Arslan
- Departments of Obstetrics and Gynecology and Population Health, New York University Grossman School of Medicine, New York, NY
| | - Julie Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | | | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Chu Chen
- Program in Epidemiology and the Women's Health Initiative Clinical Coordinating Center, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Michael P A Davies
- Molecular & Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Brenda Diergaarde
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA; UPMC Hillman Cancer Centre, Pittsburgh, PA
| | - John K Field
- Molecular & Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Mikael Johansson
- Department of Radiation Sciences, Oncology, Umea University, Umea, Sweden
| | - Michael Jones
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - 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
| | - Stephen Lam
- Integrative Oncology, British Columbia Cancer Agency, Vancouver, Canada
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Arnulf Langhammer
- HUNT Research Center, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Linda M Liao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Geoffrey Liu
- Computational Biology and Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Reza Malekzadeh
- Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Australia; School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Luis M Montuenga
- Center of Applied Medical Research (CIMA) and Schools of Sciences and Medicine, University of Navarra, Pamplona, Spain; IDISNA, Pamplona, Spain; CIBERONC, Madrid, Spain
| | - Thomas Rohan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Howard D Sesso
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | - Mahdi Sheikh
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Rashmi Sinha
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Martin C Tammemägi
- Department of Health Sciences, Brock University, St. Cathaarines, ON, Canada; Prevention and Cancer Control, Ontario Health, Toronto, ON, Canada
| | - Lesley F Tinker
- Women's Health Initiative Clinical Coordinating Center, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Ying Wang
- American Cancer Society, Atlanta, GA
| | - Renwei Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Emily White
- Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - David Wilson
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate Schoolf of Public Health, University of Pittsburgh, Pittsburgh, PA; UPMC Hillman Cancer Centre, Pittsburgh, PA
| | - Xuehong Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX
| | - Paul Brennan
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
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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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21
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Dickson JL, Hall H, Horst C, Tisi S, Verghese P, Worboys S, Perugia A, Rusius J, Mullin AM, Teague J, Farrelly L, Bowyer V, Gyertson K, Bojang F, Levermore C, Anastasiadis T, McCabe J, Devaraj A, Nair A, Navani N, Hackshaw A, Quaife SL, Janes SM. Utilisation of primary care electronic patient records for identification and targeted invitation of individuals to a lung cancer screening programme. Lung Cancer 2022; 173:94-100. [PMID: 36179541 PMCID: PMC10533413 DOI: 10.1016/j.lungcan.2022.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/27/2022] [Accepted: 09/13/2022] [Indexed: 11/22/2022]
Abstract
Lung cancer screening (LCS) eligibility is largely determined by tobacco consumption. Primary care smoking data could guide LCS invitation and eligibility assessment. We present observational data from the SUMMIT Study, where individual self-reported smoking status was concordant with primary care records in 75.3%. However, 10.3% demonstrated inconsistencies between historic and most recent smoking status documentation. Quantified tobacco consumption was frequently missing, precluding direct LCS eligibility assessment. Primary care recorded "ever-smoker" status, encompassing both recent and historic documentation, can be used to target LCS invitation. Identifying those with missing or erroneous "never-smoker" smoking status is crucial for equitable invitation to LCS.
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Affiliation(s)
- Jennifer L Dickson
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Helen Hall
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Carolyn Horst
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sophie Tisi
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Priyam Verghese
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | | | | | | | - Anne-Marie Mullin
- Cancer Research UK and UCL Cancer Trials Centre, University College London, London, UK
| | - Jonathan Teague
- Cancer Research UK and UCL Cancer Trials Centre, University College London, London, UK
| | - Laura Farrelly
- Cancer Research UK and UCL Cancer Trials Centre, University College London, London, UK
| | - Vicky Bowyer
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Kylie Gyertson
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Fanta Bojang
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Claire Levermore
- University College London Hospitals NHS Foundation Trust, London, UK
| | | | - John McCabe
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, London, UK; National Heart and Lung Institute, Imperial College, London, UK
| | - Arjun Nair
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK; University College London Hospitals NHS Foundation Trust, London, UK
| | - Allan Hackshaw
- Cancer Research UK and UCL Cancer Trials Centre, University College London, London, UK
| | - Samantha L Quaife
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK; University College London Hospitals NHS Foundation Trust, London, UK.
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22
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Robbins HA, Zahed H, Lebrett MB, Balata H, Johansson M, Sharman A, Evans DG, Crosbie EJ, Booton R, Landy R, Crosbie PAJ. Explaining differences in the frequency of lung cancer detection between the National Lung Screening Trial and community-based screening in Manchester, UK. Lung Cancer 2022; 171:61-64. [PMID: 35917648 PMCID: PMC9790152 DOI: 10.1016/j.lungcan.2022.07.017] [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: 06/15/2022] [Accepted: 07/21/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND The frequency of lung cancer detection in the Manchester Lung Health Checks (MLHCs), a community-based screening service, was higher than in the National Lung Screening Trial (NLST) over two screening rounds. We aimed to identify the potential reasons for this difference. METHODS We analyzed individual-level data from NLST and MLHCs, restricting to MLHCs participants who met NLST eligibility criteria. We calculated 'detection ratios' comparing the frequency of lung cancer detection in MLHCs vs NLST, first after excluding NLST participants ineligible by MLHC eligibility criteria (6-year lung cancer risk ≥ 1.51 %), and then after standardization to remove the influence of different distributions of baseline lung cancer risk. RESULTS Among the 1,079 MLHCs participants who met NLST eligibility criteria, 4.7% were diagnosed with lung cancer over two screening rounds compared with 1.7% in NLST, giving an initial detection ratio of 2.6 (95%CI 2.2-3.0). This was reduced to 2.2 (95%CI 1.3-2.3) after imposing the MLHCs eligibility criterion on NLST, and further to 1.6 (95%CI 1.2-2.1) after removing the influence of different risk distributions. In stratified analyses, the standardized detection ratio was particularly elevated in individuals who were older, living in areas of high socioeconomic disadvantage, or had an FEV/FVC ratio less than 60. CONCLUSIONS The 2.6-fold higher lung cancer detection in the community-based MLHCs vs NLST is partly explained by differences in eligibility criteria and baseline risk distributions. The residual 60% increase may relate to higher detection in certain risk groups, including older participants, those with more obstructive lung disease, and those living in areas of socioeconomic disadvantage.
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Affiliation(s)
- Hilary A Robbins
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
| | - Hana Zahed
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Mikey B Lebrett
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK; Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Haval Balata
- Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK; Manchester Thoracic Oncology Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Anna Sharman
- Manchester Thoracic Oncology Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - D Gareth Evans
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK; Division of Evolution and Genomic Sciences, University of Manchester, Manchester, UK
| | - Emma J Crosbie
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK; Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Richard Booton
- Manchester Thoracic Oncology Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Rebecca Landy
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Philip A J Crosbie
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK; Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
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23
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Risk perception and disease knowledge in attendees of a community-based lung cancer screening programme. Lung Cancer 2022; 168:1-9. [DOI: 10.1016/j.lungcan.2022.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/06/2022] [Accepted: 04/04/2022] [Indexed: 12/24/2022]
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24
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Burzic A, O’Dowd EL, Baldwin DR. The Future of Lung Cancer Screening: Current Challenges and Research Priorities. Cancer Manag Res 2022; 14:637-645. [PMID: 35210860 PMCID: PMC8859535 DOI: 10.2147/cmar.s293877] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/30/2022] [Indexed: 11/30/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, primarily because most people present when the stage is too advanced to offer any reasonable chance of cure. Over the last two decades, evidence has accumulated to show that early detection of lung cancer, using low-radiation dose computed tomography, in people at higher risk of the condition reduces their mortality. Many countries are now making progress with implementing programmes, although some have concerns about cost-effectiveness. Lung cancer screening is complex, and many factors influence clinical and cost-effectiveness. It is important to develop strategies to optimise each element of the intervention from selection and participation through optimal scanning, management of findings and treatment. The overall aim is to maximise benefits and minimise harms. Additional integrated interventions must include at least smoking cessation. In this review, we summarize the evidence that has accumulated to guide optimisation of lung cancer screening, discuss the remaining open questions about the best approach and identify potential barriers to successful implementation.
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Affiliation(s)
- Amna Burzic
- Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
| | - Emma L O’Dowd
- Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
- Division of Medicine, University of Nottingham, Nottingham, NG5 1PB, UK
| | - David R Baldwin
- Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
- Division of Medicine, University of Nottingham, Nottingham, NG5 1PB, UK
- Correspondence: David R Baldwin, Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK, Tel +44 115 9691169, Fax +44 115 9627723, Email
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25
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Robbins HA, Cheung LC, Chaturvedi AK, Baldwin DR, Berg CD, Katki HA. Management of Lung Cancer Screening Results Based on Individual Prediction of Current and Future Lung Cancer Risks. J Thorac Oncol 2022; 17:252-263. [PMID: 34648946 PMCID: PMC10186153 DOI: 10.1016/j.jtho.2021.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/03/2021] [Accepted: 10/04/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVES We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals. METHODS We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal). RESULTS Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol. CONCLUSIONS Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.
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Affiliation(s)
| | - Li C. Cheung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Anil K. Chaturvedi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | | | - Christine D. Berg
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Hormuzd A. Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
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26
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Pastorino U, Boeri M, Sestini S, Sabia F, Milanese G, Silva M, Suatoni P, Verri C, Cantarutti A, Sverzellati N, Corrao G, Marchianò A, Sozzi G. Baseline computed tomography screening and blood microRNA predict lung cancer risk and define adequate intervals in the BioMILD trial. Ann Oncol 2022; 33:395-405. [DOI: 10.1016/j.annonc.2022.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 12/17/2022] Open
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