Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography

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 ...

Contemporary issues in the implementation of lung cancer screening

“…Black Americans have higher lung cancer incidence than White Americans, but they typically smoke with less intensity. Fewer females than males would be eligible for screening with a ⩾30 pack-years eligibility requirement, as they typically accumulate fewer pack-years than male smokers [56,57]. The National Comprehensive Cancer Network in the US has endorsed using the PLCOm2012 with ⩾1.3%/6-year risk threshold in its group 2 criteria for screening.…”

“…This would better address racial/ethnic and gender inequalities without increasing the number of low-risk individuals being screened by lowering the age and pack-years criteria, which would have significant implications in increasing potential harms from screening, resource utilisation and costs, but the USPSTF has been reluctant to adopt the use of lung cancer risk prediction models. Some of the reasons cited for exclusion include that risk prediction models select older individuals who have more comorbidities, more competing causes of death and have fewer life-years gained and that risk models are too difficult to use and may serve as a barrier to screening [56]. Among PLCO trial participants who were NLST criteria eligible, when low-risk NLST-eligible individuals were excluded (PLCOm2012 risk <1.5%/6 years, observed actual risk 0.8%/6 years), those who were in the NLST group or in the PLCOm2012 ⩾1.5% high-risk group had comorbidity counts of 0.99 versus 1.01 and competing deaths in 5 years of 6.6 per 100 versus 6.7 per 100, respectively [41].…”

“…These differences are small. In the Cancer Intervention and Surveillance Modeling Network (CISNET) model, the life-years gained per 100 000 individuals screened is slightly higher with PLCOm2012 compared to USPSTF2013 (4982 versus 4882) [56]. There is little strong evidence to support the life expectancy of screenees selected by PLCOm2012 being substantially shorter than those with USPSTF criteria.…”

Age, Sex, Smoking, and Race

“…Although NH Black men had the lowest E-I ratio by most criteria, the greatest difference was observed in younger (ages 50-64) NH Black men. Lowering the age to initiate screening to 50 and the cumulative smoking history to $20 packyears, as recently recommended by the United States Preventative Services Task Force (USPSTF), 8 increased eligibility for LCS in NH-Black men and women, but disparities persisted between LCS eligibility and lung cancer incidence.…”

“…12 The use of risk prediction models to improve eligibility for LCS is perceived to be potentially more burdensome to primary care providers, because it requires input of clinical variables. 8 However, widespread use may be aided by embedding required factors within the electronic medical record, prompts to assess patient eligibility, and tools to allow patients to enter and assess eligibility remotely before enrollment in an LCS program.…”