Lung Cancer Screening and Smoking Cessation: Never Too Early or Too Late

Lung Cancer Screening and Smoking Cessation: Never Too Early or Too Late With lung cancer being the leading cause of cancer mortality worldwide, clinicians are eager for interventions that improve early detection (1). Lung cancer screening with low-dose computed tomography (LDCT) is now being broadly implemented across the United States (2,3). This is driven by results from the National Lung Screening Trial (NLST), which demonstrated three fewer deaths from lung cancer for every 1000 high-risk individuals who underwent screening with LDCT for three years, compared with three annual rounds of screening with chest radiography (4). Guidelines from multiple organizations including the United States Preventive Services Task Force (USPSTF), the American Cancer Society, and others now recommend lung cancer screening for high-risk patients, with eligibility criteria that are largely based on the NLST criteria (5). Importantly, the Centers for Medicare and Medicaid Services (CMS) now covers the cost of LDCT for current or former smokers who are 55 to 77 years old, have at least 30 pack-years of tobacco exposure, and still smoke or quit within the last 15 years. However, there continues to be debate over the selection of appropriate candidates for lung cancer screening. It is unclear whether LDCT screening for individuals who do not meet NLST criteria but have other lung cancer risk factors is a reasonable approach (5). Based on recent data from the National Health Interview Survey, there are close to 100 million current and former smokers in the United States (6). There are likely to be NLST-ineligible individuals who have an elevated risk of lung cancer who may benefit from screening, such as those who smoke less than the required number of pack-years but who nonetheless have years of cumulative smoking exposure, or those who were heavy smokers and quit smoking more than 15 years ago (7). Therefore, a considerable percentage of current and former smokers who will receive a diagnosis of lung cancer may not be eligible for lung cancer screening (8). In this issue, Tindle et al. (9) present findings relating lifetime smoking history to the risk of lung cancer, with an emphasis on exploring the relative risk reduction for incident lung cancer in former vs current smokers. The authors evaluated a total of 8907 participants in the Framingham Health Study, from both the original cohort (initiated in 1948, n = 3905) and the subsequent offspring cohort (initiated in 1971, n = 5002). Detailed smoking history was collected at multiple time points for both cohorts, including age of initiation, usual number of cigarettes per day, and age at quitting for former smokers. The multiple assessments allowed for changes in participant smoking status over time (current, former, never). Lung cancer incidence was ascertained and verified through 2013. The authors found that the majority of lung cancers (>90%) occurred in current and former heavy smokers, defined as 21.3 or more pack-years. Multivariable models showed that the risk of lung cancer was lower among former heavy smokers as compared with current heavy smokers, and this reduced risk was detectable within five years since quitting (YSQ). The authors also examined former heavy smokers vs never smokers, finding that former heavy smokers had a statistically significant persistently elevated risk of lung cancer even after 25 YSQ compared with never smokers (hazard ratio = 3.85, 95% confidence interval = 1.80 to 8.26). Just over half of current and former smokers who developed lung cancer met the current CMS screening eligibility requirements. Notably, 40.8% of lung cancer diagnoses in former smokers occurred in those with more than 15 YSQ. These results confirm and extend prior findings from a case–control study in southwest England that reported age-adjusted risk ratios for lung cancer incidence of 0.66, 0.44, and 0.20 for male former smokers who had quit smoking for less than 10, 10 to 19, and 20 to 29 years, respectively, compared with male current smokers (10). Similar results were observed in female former smokers. A strength of these analyses reported by Tindle et al. is the inclusion of multiple assessments of smoking status (median = 16 for the original cohort, median = 9 for the offspring) over a median follow-up time of 28.7 years. These and other dynamic predictors (eg, pack-years) were updated at each exam and were modeled as time-varying covariates. Few modern studies capture such detailed longitudinal data. A sensitivity analysis limiting the number of assessments to either six or two to examine the effect of fewer assessments over time found that use of fewer assessments may lead to underestimation of lung cancer risk in former heavy smokers. Overall, these findings are a meaningful contribution and will help shape our evolving understanding of case selection for lung cancer screening. The finding of elevated risk of lung cancer in former heavy smokers as far out as 25 YSQ is important. Future research should include these data in simulation models examining varying cut-points of pack-years, YSQ, and other relevant predictors of lung cancer risk to help refine and update screening criteria. The USPSTF lung cancer screening guideline was informed by simulation models of lung cancer risk, modeling scenarios that differed from the NLST eligibility criteria (5). There are limitations of these results to be considered, the most important of which is the sociodemographic constraints of the Framingham cohort. Additionally, information on other lung cancer risk factors including family history of disease and relevant comorbidities was not captured. The distinction between high-level policy (eg, CMS rule) and individual clinical practice is highly relevant to lung cancer screening. Clinical practice often reveals the limitations of adopting a specific screening threshold. An individualized approach to lung cancer screening that incorporates multiple risk factors outside of smoking history and age, such as family history and underlying disease, may be more attractive to clinicians who want to provide the most appropriate recommendation for their patient. Lung cancer risk prediction models incorporate multiple factors beyond age and smoking history (11). However, results from these individualized approaches may not align with health insurance coverage policies. As the authors note, there are valid concerns about expanding current lung cancer screening eligibility. It is unclear if extending the YSQ threshold would result in a similar lung cancer mortality benefit to the NLST. Moreover, there is ongoing disagreement about whether the goal of a high-quality screening program is to identify the greatest number of treatable cancers or to maximize efficiency by providing screening services to those with the highest individual lung cancer risk. Ongoing research that incorporates new findings such as those presented by Tindle et al. will provide guidance as we move forward. Notes Affiliation of authors: Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA. MKG received research support from Medial EarlySign, LLC, to develop computer models of lung cancer risk. EEH has no conflicts of interest to disclose. References 1 International Agency for Research on Cancer. W: GLOBOCAN 2012: estimated cancer incidence, mortality and prevalence worldwide. http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx. Accessed May 10, 2018. 2 Kinsinger LS , Anderson C , Kim J , et al. . Implementation of lung cancer screening in the Veterans Health Administration . JAMA Intern Med. 2017 ; 177 : 399 – 406 . Google Scholar Crossref Search ADS PubMed 3 Gould MK , Sakoda LC , Ritzwoller DP , et al. . Monitoring lung cancer screening use and outcomes at four cancer research network sites . Ann Am Thorac Soc. 2017 ; 14 : 1827 – 1835 . Google Scholar Crossref Search ADS PubMed 4 Aberle DR , Adams AM , Berg CD , et al. . Reduced lung-cancer mortality with low-dose computed tomographic screening . N Engl J Med. 2011 ; 365 : 395 – 409 . Google Scholar Crossref Search ADS PubMed 5 Tanoue LT , Tanner NT , Gould MK , et al. . Lung cancer screening . Am J Respir Crit Care Med. 2015 ; 191 : 19 – 33 . Google Scholar Crossref Search ADS PubMed 6 Ward BW , Clarke TC , Nugent CN , et al. . Early Release of Selected Estimates Based on Data From the 2015 National Health Interview Survey. CDC ; 2016 . https://www.cdc.gov/nchs/data/nhis/earlyrelease/earlyrelease201605.pdf. Accessed May 10, 2018. 7 Katki HA , Kovalchik SA , Berg CD , et al. . Development and validation of risk models to select ever-smokers for CT lung cancer screening . JAMA. 2016 ; 315 : 2300 – 2311 . Google Scholar Crossref Search ADS PubMed 8 Tammemagi MC , Katki HA , Hocking WG , et al. . Selection criteria for lung-cancer screening . N Engl J Med. 2013 ; 368 : 728 – 736 . Google Scholar Crossref Search ADS PubMed 9 Tindle HA, Stevenson Duncan M , Greevy RA , et al. . Lifetime smoking history and risk of lung cancer: Results from the Framingham Heart Study . J Natl Cancer Inst. 2018 ; 110 ( 11 ): 1201 – 1207 . 10 Peto R , Darby S , Deo H , et al. . Smoking, smoking cessation, and lung cancer in the UK since 1950: Combination of national statistics with two case-control studies . BMJ. 2000 ; 321 : 323 – 329 . Google Scholar Crossref Search ADS PubMed 11 Bach PB , Kattan MW , Thornquist MD , et al. . Variations in lung cancer risk among smokers . J Natl Cancer Inst. 2003 ; 95 : 470 – 478 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JNCI: Journal of the National Cancer Institute Oxford University Press

Lung Cancer Screening and Smoking Cessation: Never Too Early or Too Late

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
ISSN
0027-8874
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1460-2105
D.O.I.
10.1093/jnci/djy083
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Abstract

With lung cancer being the leading cause of cancer mortality worldwide, clinicians are eager for interventions that improve early detection (1). Lung cancer screening with low-dose computed tomography (LDCT) is now being broadly implemented across the United States (2,3). This is driven by results from the National Lung Screening Trial (NLST), which demonstrated three fewer deaths from lung cancer for every 1000 high-risk individuals who underwent screening with LDCT for three years, compared with three annual rounds of screening with chest radiography (4). Guidelines from multiple organizations including the United States Preventive Services Task Force (USPSTF), the American Cancer Society, and others now recommend lung cancer screening for high-risk patients, with eligibility criteria that are largely based on the NLST criteria (5). Importantly, the Centers for Medicare and Medicaid Services (CMS) now covers the cost of LDCT for current or former smokers who are 55 to 77 years old, have at least 30 pack-years of tobacco exposure, and still smoke or quit within the last 15 years. However, there continues to be debate over the selection of appropriate candidates for lung cancer screening. It is unclear whether LDCT screening for individuals who do not meet NLST criteria but have other lung cancer risk factors is a reasonable approach (5). Based on recent data from the National Health Interview Survey, there are close to 100 million current and former smokers in the United States (6). There are likely to be NLST-ineligible individuals who have an elevated risk of lung cancer who may benefit from screening, such as those who smoke less than the required number of pack-years but who nonetheless have years of cumulative smoking exposure, or those who were heavy smokers and quit smoking more than 15 years ago (7). Therefore, a considerable percentage of current and former smokers who will receive a diagnosis of lung cancer may not be eligible for lung cancer screening (8). In this issue, Tindle et al. (9) present findings relating lifetime smoking history to the risk of lung cancer, with an emphasis on exploring the relative risk reduction for incident lung cancer in former vs current smokers. The authors evaluated a total of 8907 participants in the Framingham Health Study, from both the original cohort (initiated in 1948, n = 3905) and the subsequent offspring cohort (initiated in 1971, n = 5002). Detailed smoking history was collected at multiple time points for both cohorts, including age of initiation, usual number of cigarettes per day, and age at quitting for former smokers. The multiple assessments allowed for changes in participant smoking status over time (current, former, never). Lung cancer incidence was ascertained and verified through 2013. The authors found that the majority of lung cancers (>90%) occurred in current and former heavy smokers, defined as 21.3 or more pack-years. Multivariable models showed that the risk of lung cancer was lower among former heavy smokers as compared with current heavy smokers, and this reduced risk was detectable within five years since quitting (YSQ). The authors also examined former heavy smokers vs never smokers, finding that former heavy smokers had a statistically significant persistently elevated risk of lung cancer even after 25 YSQ compared with never smokers (hazard ratio = 3.85, 95% confidence interval = 1.80 to 8.26). Just over half of current and former smokers who developed lung cancer met the current CMS screening eligibility requirements. Notably, 40.8% of lung cancer diagnoses in former smokers occurred in those with more than 15 YSQ. These results confirm and extend prior findings from a case–control study in southwest England that reported age-adjusted risk ratios for lung cancer incidence of 0.66, 0.44, and 0.20 for male former smokers who had quit smoking for less than 10, 10 to 19, and 20 to 29 years, respectively, compared with male current smokers (10). Similar results were observed in female former smokers. A strength of these analyses reported by Tindle et al. is the inclusion of multiple assessments of smoking status (median = 16 for the original cohort, median = 9 for the offspring) over a median follow-up time of 28.7 years. These and other dynamic predictors (eg, pack-years) were updated at each exam and were modeled as time-varying covariates. Few modern studies capture such detailed longitudinal data. A sensitivity analysis limiting the number of assessments to either six or two to examine the effect of fewer assessments over time found that use of fewer assessments may lead to underestimation of lung cancer risk in former heavy smokers. Overall, these findings are a meaningful contribution and will help shape our evolving understanding of case selection for lung cancer screening. The finding of elevated risk of lung cancer in former heavy smokers as far out as 25 YSQ is important. Future research should include these data in simulation models examining varying cut-points of pack-years, YSQ, and other relevant predictors of lung cancer risk to help refine and update screening criteria. The USPSTF lung cancer screening guideline was informed by simulation models of lung cancer risk, modeling scenarios that differed from the NLST eligibility criteria (5). There are limitations of these results to be considered, the most important of which is the sociodemographic constraints of the Framingham cohort. Additionally, information on other lung cancer risk factors including family history of disease and relevant comorbidities was not captured. The distinction between high-level policy (eg, CMS rule) and individual clinical practice is highly relevant to lung cancer screening. Clinical practice often reveals the limitations of adopting a specific screening threshold. An individualized approach to lung cancer screening that incorporates multiple risk factors outside of smoking history and age, such as family history and underlying disease, may be more attractive to clinicians who want to provide the most appropriate recommendation for their patient. Lung cancer risk prediction models incorporate multiple factors beyond age and smoking history (11). However, results from these individualized approaches may not align with health insurance coverage policies. As the authors note, there are valid concerns about expanding current lung cancer screening eligibility. It is unclear if extending the YSQ threshold would result in a similar lung cancer mortality benefit to the NLST. Moreover, there is ongoing disagreement about whether the goal of a high-quality screening program is to identify the greatest number of treatable cancers or to maximize efficiency by providing screening services to those with the highest individual lung cancer risk. Ongoing research that incorporates new findings such as those presented by Tindle et al. will provide guidance as we move forward. Notes Affiliation of authors: Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA. MKG received research support from Medial EarlySign, LLC, to develop computer models of lung cancer risk. EEH has no conflicts of interest to disclose. References 1 International Agency for Research on Cancer. W: GLOBOCAN 2012: estimated cancer incidence, mortality and prevalence worldwide. http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx. Accessed May 10, 2018. 2 Kinsinger LS , Anderson C , Kim J , et al. . Implementation of lung cancer screening in the Veterans Health Administration . JAMA Intern Med. 2017 ; 177 : 399 – 406 . Google Scholar Crossref Search ADS PubMed 3 Gould MK , Sakoda LC , Ritzwoller DP , et al. . Monitoring lung cancer screening use and outcomes at four cancer research network sites . Ann Am Thorac Soc. 2017 ; 14 : 1827 – 1835 . Google Scholar Crossref Search ADS PubMed 4 Aberle DR , Adams AM , Berg CD , et al. . Reduced lung-cancer mortality with low-dose computed tomographic screening . N Engl J Med. 2011 ; 365 : 395 – 409 . Google Scholar Crossref Search ADS PubMed 5 Tanoue LT , Tanner NT , Gould MK , et al. . Lung cancer screening . Am J Respir Crit Care Med. 2015 ; 191 : 19 – 33 . Google Scholar Crossref Search ADS PubMed 6 Ward BW , Clarke TC , Nugent CN , et al. . Early Release of Selected Estimates Based on Data From the 2015 National Health Interview Survey. CDC ; 2016 . https://www.cdc.gov/nchs/data/nhis/earlyrelease/earlyrelease201605.pdf. Accessed May 10, 2018. 7 Katki HA , Kovalchik SA , Berg CD , et al. . Development and validation of risk models to select ever-smokers for CT lung cancer screening . JAMA. 2016 ; 315 : 2300 – 2311 . Google Scholar Crossref Search ADS PubMed 8 Tammemagi MC , Katki HA , Hocking WG , et al. . Selection criteria for lung-cancer screening . N Engl J Med. 2013 ; 368 : 728 – 736 . Google Scholar Crossref Search ADS PubMed 9 Tindle HA, Stevenson Duncan M , Greevy RA , et al. . Lifetime smoking history and risk of lung cancer: Results from the Framingham Heart Study . J Natl Cancer Inst. 2018 ; 110 ( 11 ): 1201 – 1207 . 10 Peto R , Darby S , Deo H , et al. . Smoking, smoking cessation, and lung cancer in the UK since 1950: Combination of national statistics with two case-control studies . BMJ. 2000 ; 321 : 323 – 329 . Google Scholar Crossref Search ADS PubMed 11 Bach PB , Kattan MW , Thornquist MD , et al. . Variations in lung cancer risk among smokers . J Natl Cancer Inst. 2003 ; 95 : 470 – 478 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

JNCI: Journal of the National Cancer InstituteOxford University Press

Published: Nov 1, 2018

References

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