Pianpian Cao

Background: Replacing the pack-year criterion for lung cancer screening with smoking duration has been proposed to reduce racial disparities in eligibility. However, no studies have evaluated the potential long-term benefits and harms of duration-based criteria. We conducted a comparative modeling study to evaluate the effectiveness of duration-based eligibility vs that from current guidelines and other alternative criteria. Methods: We used 3 Cancer Intervention and Surveillance Modeling Network models to evaluate the performance of duration-based screening with various smoking duration thresholds (15-40 years in 5-year increments) for the 1960 and 1970 US birth cohorts. We first examined age-specific eligibility patterns of duration-based strategies vs pack-year and risk-based criteria. We then evaluated the performance of different strategies, comparing the resulting number of screens, lung cancer deaths averted, life-years gained, false-positive screens, and overdiagnosed cases. We compared the strategies’ efficiency using lung cancer deaths averted and life-years gained per screen and the benefit-to-harm ratios using lung cancer deaths averted and life-years gained per overdiagnosed case. Results: Risk-based criteria resulted in the most lung cancer deaths averted and life-years gained but also in more overdiagnosed cases. Duration-based strategies with a 35-year cutoff achieved comparable lung cancer deaths averted and life-years gained to current US guidelines and resulted in similar false-positive and overdiagnosed cases per screen. Duration-based scenarios with a 20-year cutoff required substantially more screenings but yielded only modest additional lung cancer deaths averted and life-years gained, resulting in lower benefits per screen than current guidelines. Conclusion: Duration-based screening may be as efficient as current US guidelines. Given their potential to reduce disparities in eligibility shown in recent studies and simpler implementation, duration-based criteria warrant consideration.

Background: There is little guidance on how to personalize recommendations for lung cancer screening that accounts for the variation in expected net benefit from screening. We sought to explore the individual and population implications of identifying net benefit thresholds where lung cancer screening could be encouraged, discouraged, or offered as an option through neutral shared decision making due to screening being highly preference sensitive. Methods: With a simulated US population of 40- to 80-year-old individuals who had ever smoked, we used microsimulation to estimate individualized quality-adjusted life-years saved with lung cancer screening. We then identified 2 net benefit thresholds for lung cancer screening that account for a range of patient preferences and scientific uncertainties and compared this approach with the current United States Preventive Services Task Force (USPSTF) guidelines. Results: Our simulated population included 59 million people. In total, 15 million were USPSTF eligible; of those, 53% (8 million) maintained net benefit, even after accounting for unfavorable preferences about screening. Of the USPSTF population, 3% (450 000) are considered low net benefit (routinely discourage) and 47% (7 million) are in an intermediate gray area, where net benefit depends on patient preferences about lung cancer screening (offer-neutral shared decision making). Among adults who had ever smoked, 2.5 million are high net benefit but excluded by current USPSTF criteria; 20.5 million US adults who had ever smoked are intermediate net benefit but currently excluded by USPSTF criteria. Conclusions: We estimate that half of the USPSTF lung cancer screening–eligible population is in a high-net-benefit group where lung cancer screening could be routinely encouraged. Current lung cancer screening eligibility criteria likely exclude many ever-smokers who are high net benefit and many more with intermediate net benefit.

Background: Lung cancer incidence has been decreasing in the United States, largely due to smoking reductions. However, adenocarcinoma incidence has been relatively stable compared with other histological subtypes. Histology-specific lung cancer incidence varies by key sociodemographic characteristics, but trends are not well characterized. Methods: SEER 17 registry data was used to calculate annual age-adjusted lung cancer incidence over 2000-2019 by histology stratified by individual-level sex and race/ethnicity and county-level education, poverty, or urbanicity. Histology was categorized into 4 groups: adenocarcinoma, small cell, squamous cell, and other histologies. Age-adjusted incidence rates were computed using the 2000 US Standard Population. Incidence trends were characterized using Joinpoint regression. Results: For most histological subtypes, lung cancer incidence has been decreasing since 2000 in both sexes and all racial/ethnic groups, with some variations. However, lung adenocarcinoma incidence was relatively constant. Lung cancer incidence decreases with increasing education and income. It is lower in urban vs rural areas across histological subtypes, except for adenocarcinoma among females. Counties with higher education levels or lower poverty rates experienced faster declines in small cell and squamous cell lung cancer incidence in recent years. The incidence of small cell and squamous cell lung cancer has been decreasing more rapidly in urban than rural areas. Conclusion: Disadvantaged groups have higher lung cancer rates and slower decreases in incidence over time for most histological subtypes, resulting in widening disparities. This highlights the need for targeted tobacco and lung cancer prevention strategies to accelerate decreases in vulnerable populations.

Background: Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. Design: Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. Results: Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%–98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%–64.6%) and 2 to 4 y (MR: 28.8%–43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%–91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%–48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers. Conclusions: Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals). Implications: Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers. Highlights: Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess. This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics. Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions. Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.

Background: Lung cancer is the leading cause of cancer deaths and disproportionately affects self-identified Black or African American (“Black”) people, especially considering their relatively low self-reported smoking intensity rates. This study aimed to determine the relative impact of smoking history and lung cancer incidence risk, histology, stage, and survival on these disparities. Methods: We used 2 lung cancer models (MichiganLung-All Races and MichiganLung-Black) to understand why Black people have higher rates of lung cancer deaths. We studied how different factors, such as smoking behaviors, cancer development, histology, stage at diagnosis, and lung cancer survival, contribute to these differences. Results: Adjusted for smoking history, approximately 90% of the difference in lung cancer deaths between the overall and Black populations (born in 1960) was the result of differences in the risk of getting lung cancer. Differences in the histology and stage of lung cancer and survival had a small impact (4% to 6% for each). Similar results were observed for the 1950 and 1970 birth cohorts, regardless of their differences in smoking patterns from the 1960 cohort. Conclusions: After taking smoking into account, the higher rate of lung cancer deaths in Black people can mostly be explained by differences in the risk of developing lung cancer. As lung cancer treatments and detection improve, however, other factors may become more important in determining differences in lung cancer mortality between the Black and overall populations. To prevent current disparities from becoming worse, it is important to make sure that these improvements are available to everyone in an equitable way.

In its 2021 update of lung cancer screening recommendations, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that selected people on the basis of risk and emphasized the need ... Visual A Cost-Effectiveness Analysis of Risk Model–Based Lung Cancer Screening. In its 2021 update of lung cancer screening recommendations, the U.S. Preventive Services Task Force (USPSTF... Background: In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer r...