Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography: Modeling Study for the US Preventive Services Task Force

Abstract

Importance: The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years.

Objective: To inform the USPSTF guidelines by estimating the benefits and harms associated with various low-dose computed tomography (LDCT) screening strategies.

Design, setting, and participants: Comparative simulation modeling with 4 lung cancer natural history models for individuals from the 1950 and 1960 US birth cohorts who were followed up from aged 45 through 90 years.

Exposures: Screening with varying starting ages, stopping ages, and screening frequency. Eligibility criteria based on age, cumulative pack-years, and years since quitting smoking (risk factor-based) or on age and individual lung cancer risk estimation using risk prediction models with varying eligibility thresholds (risk model-based). A total of 1092 LDCT screening strategies were modeled. Full uptake and adherence were assumed for all scenarios.

Main outcomes and measures: Estimated lung cancer deaths averted and life-years gained (benefits) compared with no screening. Estimated lifetime number of LDCT screenings, false-positive results, biopsies, overdiagnosed cases, and radiation-related lung cancer deaths (harms).

Results: Efficient screening programs estimated to yield the most benefits for a given number of screenings were identified. Most of the efficient risk factor-based strategies started screening at aged 50 or 55 years and stopped at aged 80 years. The 2013 USPSTF-recommended criteria were not among the efficient strategies for the 1960 US birth cohort. Annual strategies with a minimum criterion of 20 pack-years of smoking were efficient and, compared with the 2013 USPSTF-recommended criteria, were estimated to increase screening eligibility (20.6%-23.6% vs 14.1% of the population ever eligible), lung cancer deaths averted (469-558 per 100 000 vs 381 per 100 000), and life-years gained (6018-7596 per 100 000 vs 4882 per 100 000). However, these strategies were estimated to result in more false-positive test results (1.9-2.5 per person screened vs 1.9 per person screened with the USPSTF strategy), overdiagnosed lung cancer cases (83-94 per 100 000 vs 69 per 100 000), and radiation-related lung cancer deaths (29.0-42.5 per 100 000 vs 20.6 per 100 000). Risk model-based vs risk factor-based strategies were estimated to be associated with more benefits and fewer radiation-related deaths but more overdiagnosed cases.

Conclusions and relevance: Microsimulation modeling studies suggested that LDCT screening for lung cancer compared with no screening may increase lung cancer deaths averted and life-years gained when optimally targeted and implemented. Screening individuals at aged 50 or 55 years through aged 80 years with 20 pack-years or more of smoking exposure was estimated to result in more benefits than the 2013 USPSTF-recommended criteria and less disparity in screening eligibility by sex and race/ethnicity.

Figure 1.. Number of LDCT Screening Examinations vs. the Number of Lung Cancer Deaths Averted in Each of the 288 Risk Factor–Based Strategies Evaluated by the 4 CISNET Models—1960 Birth Cohort

Note: Each point represents a different scenario, and the line represents the estimated efficient frontier per model. Strategies vary by age at starting and stopping screening, frequency, pack-years criterion, and years since quitting smoking (eTable 2). The colors differentiate strategies by frequency (annual–A vs. biennial–B) and the age at stopping screening (75, 77, 80 years). The no-screening (black dot) and the 2013 USPSTF-recommended (“⊗” mark) scenarios are highlighted. Four CISNET lung cancer screening simulation models from different institutions were used for the analysis: Microsimulation Screening Analysis (MISCAN)-Lung Model from Erasmus University Medical Center, Massachusetts General Hospital–Harvard Medical School (MGH-HMS), the Lung Cancer Outcomes Simulation (LCOS) from Stanford University, and University of Michigan (UM). Abbreviations: CISNET=Cancer Intervention and Surveillance Modeling Network; LDCT=low-dose computed tomography; USPSTF=U.S. Preventive Services Task Force.

Figure 2.. Number of LDCT Screening Examinations vs. Life-Years Gained in Each of the 288 Risk Factor–Based Strategies Evaluated by the 4 CISNET Models—1960 Birth Cohort

Note: Each point represents a different scenario, and the line represents the estimated efficient frontier per model. Strategies vary by age at starting and stopping screening, frequency, pack-years criterion, and years since quitting smoking (eTable 2). The colors differentiate strategies by frequency (annual–A vs. biennial–B) and the age at stopping screening (75, 77, 80 years). The no-screening (black dot) and the 2013 USPSTF-recommended (“⊗” mark) scenarios are highlighted. Four CISNET lung cancer screening simulation models from different institutions were used for the analysis: Microsimulation Screening Analysis (MISCAN)-Lung Model from Erasmus University Medical Center, Massachusetts General Hospital–Harvard Medical School (MGH-HMS), the Lung Cancer Outcomes Simulation (LCOS) from Stanford University, and University of Michigan (UM). Abbreviations: CISNET=Cancer Intervention and Surveillance Modeling Network; LDCT=low-dose computed tomography; USPSTF=U.S. Preventive Services Task Force.

Figure 3.. Number of LDCT Screening Examinations vs. the Number of Lung Cancer Deaths Averted (Left Panels) and Life-Years Gained (Right Panels) —Average Values Across the 4 CISNET Models—1960 Birth Cohort

Note: (A) Each point represents a different risk factor-based screening scenario, and the curve represents the estimated efficient frontier for the average model. Strategies vary by age at starting and stopping screening, frequency, pack-years criterion, and years since quitting smoking (eTable 2). The colors differentiate strategies by frequency (annual–A vs. biennial–B) and the age at stopping screening (75, 77, 80 years). The no-screening (black dot), the 2013 USPSTF-recommended (“⊗” mark), and six selected consensus-efficient 20 pack-year scenarios are highlighted. The panels show all 288 risk factor–based strategies but highlight (solid color points) those identified as consensus efficient (listed in eTable 3 and eTable4). The horizontal line divides strategies with less than or at least a 9% lung cancer mortality reduction. The shaded region includes those scenarios with at least a 9% lung cancer mortality reduction (listed in Tables 1 and 2). (B) Risk factor–based screening scenarios are represented with triangle points and risk model–based screening scenarios with round points. The curve represents the estimated overall efficient frontier for the average model. Risk factor–based strategies vary by age at starting and stopping screening, frequency, pack-years criterion, and years since quitting smoking (eTable 2). Risk model–based strategies vary by risk model, risk thresholds, and frequency (eTable 2). The colors differentiate strategies by frequency (annual–A vs. biennial–B). The no-screening (black dot) and the 2013 USPSTF-recommended (“⊗” mark) scenarios are highlighted. Panels show all considered strategies but highlight (solid color points) those identified as consensus efficient (listed in the full report). The vertical line represents 600 000 LDCT screens, and the horizontal line divides strategies with less than or at least a 9% lung cancer mortality reduction. The shaded region includes those scenarios with fewer than 600 000 LDCT screens per 100 000 population and providing at least a 9% lung cancer mortality reduction (listed in eTable 12 and eTable 13). Abbreviations: CISNET=Cancer Intervention and Surveillance Modeling Network; LDCT=low-dose computed tomography; USPSTF=U.S. Preventive Services Task Force.