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Structured Abstract
Importance:
The U.S. Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening recommendations.
Objective:
To inform the USPSTF by evaluating the benefits and harms of low-dose computed tomography (LDCT) screening strategies by conducting simulation modeling; comparing strategies with varying starting and stopping ages, screening frequency, and eligibility criteria (based on smoking pack-years and years since quitting smoking or based on individual lung cancer risk); and identifying efficient strategies that provide the best balance of benefits (lung cancer deaths prevented and life-years gained [LYG]) and harms for a given level of LDCT screens.
Design, Setting, and Participants:
Collaborative modeling with four lung cancer natural history models for individuals from the 1950 and 1960 birth cohorts from ages 45 to 90 years with no prior lung cancer diagnosis.
Exposures:
Screening with LDCT with varying starting ages (45, 50, 55 years), stopping ages (75, 77, 80 years), and screening frequency (annual, biennial). Eligibility criteria based on either age, cumulative pack-years (20, 25, 30, 40 years) and years since quitting smoking (10, 15, 20, 25 years) (risk factor–based strategies) or age and individual lung cancer risk estimation using three established risk prediction models (Bach, Lung Cancer Death Risk Assessment Tool, and PLCOm2012) with varying risk thresholds for eligibility (risk model–based strategies). A total of 1,093 (289 risk factor–based and 804 risk model–based) strategies were evaluated. Full uptake and adherence for all scenarios were assumed.
Main Outcomes and Measures:
Benefits: Lung cancer deaths averted and LYG compared with no screening per 100,000 population. Harms: Lifetime number of LDCT screens, false-positive results, biopsies, overdiagnosed cases, and radiation-related lung cancer deaths per 100,000 population.
Results:
We identified a set of LDCT screening programs that are efficient and result in the most lung cancer deaths averted and LYG for a given level of screening (number of LDCT screens). Most efficient risk factor–based strategies start screening at age 50 or 55 years and stop screening at the age of 80 years. Most efficient risk factor–based strategies with at least 9 percent lung cancer mortality reduction have 20 pack-years as the minimum criterion for eligibility. The 2013 USPSTF-recommended criteria, which was selected based on lung cancer deaths averted using the 1950 birth cohort, is not among the efficient strategies for the 1960 birth cohort when considering both lung cancer deaths averted and LYG. However, annual strategies with the 20 pack-years minimum criterion, starting age of 50 or 55 years and stopping age of 80 years are efficient and result in increased screening eligibility (20.6% to 23.6% eligible) and considerably more lung cancer deaths averted (469 to 558 per 100,000) and LYG (6,018 to 7,596 per 100,000) than the 2013 USPSTF-recommended strategy (14.1% eligible, 381 lung cancer deaths averted and 4,882 LYG per 100,000). However these strategies also result in more false-positive tests (1.9 to 2.5 vs. 1.9 per person screened), overdiagnosed cases (83 to 94 vs. 69 per 100,000), and radiation-related lung cancer deaths (29.0 to 42.5 vs. 20.6 per 100,000) than the 2013 USPSTF-recommended strategy. The 20 pack-year strategies result in higher relative increases vs. the 2013 USPSTF-recommended criteria in eligibility, lung cancer deaths prevented, and LYG for women than men. These strategies also result in higher relative increases compared with the 2013 USPSTF-recommended criteria in eligibility for non-Hispanic blacks, Hispanics, and American Indian/Alaska Natives than for non-Hispanic whites and Asians. Among risk model–based screening strategies, the net benefits and harms of screening strongly depend on the risk model’s specific risk thresholds. Risk model–based vs. risk factor–based strategies result in higher numbers of lung cancer deaths prevented and modest additional LYGs and induce fewer radiation-related lung cancer deaths; however, they result in more overdiagnosed cases. The general patterns observed for the 1960 birth cohort for men and women combined hold for each sex and for the 1950 birth cohort.
Limitations:
Simulations assumed 100 percent screening uptake and adherence. Relative performance of compared strategies might change if uptake and adherence differ by age or screening frequency. The models extrapolated results from short-term randomized trials with three LDCT annual screens to lifetime screening and followup. Simulations did not consider incidental findings and were restricted to the 1950 and 1960 U.S. birth cohorts.
Conclusions and Relevance:
This collaborative modeling analysis suggests that LDCT screening could lead to important reductions of lung cancer mortality and result in significant LYG when optimally targeted. In particular, screening individuals ages 50 or 55 years through 80 years with 20 or more pack-years of smoking exposure would result in more benefits than current criteria and would reduce disparities in eligibility by sex and race/ethnicity. Risk model–based screening strategies could result in higher benefits compared with risk factor–based screening strategies; however, the analysis did not consider issues of implementation and other potential challenges of risk model–based screening strategies.
Contents
Authors’ Contributions
Authors ten Haaf, Cao, and Bastani contributed equally to this report. Likewise, authors Feuer, Plevritis, de Koning, and Kong contributed equally to this report.
Suggested citation:
Meza R, Jeon J, Toumazis I, ten Haaf K, Cao P, Bastani M, Han SS, Blom EF, Jonas D, Feuer EJ, Plevritis SK, de Koning HJ, Kong CY. Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography: A Collaborative Modeling Study for the U.S. Preventive Services Task Force. AHRQ Publication No. 20-05266-EF-2. Rockville, MD: Agency for Healthcare Research and Quality; 2021.
This report is based on research conducted by the Cancer Intervention and Surveillance Modeling Network (CISNET) Lung Cancer Working Group under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. HHSA-290-2015-00011-I, Task Order No. 11) via RTI International–University of North Carolina Evidence-based Practice Center (EPC). The findings and conclusions in this document are those of the authors, who are responsible for its contents, and do not necessarily represent the views of AHRQ. Therefore, no statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
The information in this report is intended to help health care decisionmakers—patients and clinicians, health system leaders, and policymakers, among others—make well-informed decisions and thereby improve the quality of health care services. This report is not intended to be a substitute for the application of clinical judgment. Anyone who makes decisions concerning the provision of clinical care should consider this report in the same way as any medical reference and in conjunction with all other pertinent information (i.e., in the context of available resources and circumstances presented by individual patients).
This report may be used, in whole or in part, as the basis for development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. AHRQ or U.S. Department of Health and Human Services endorsement of such derivative products may not be stated or implied.
None of the investigators have any affiliations or financial involvement that conflicts with the material presented in this report.
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