Performance of Lung Cancer Risk Prediction Models in Different Racial and Ethnic Groups in the United States: Results From the Lung Cancer Cohort Consortium.

BACKGROUND

Racial and ethnic disparities are a concern in lung cancer screening.

OBJECTIVE

To investigate the performance of risk prediction models to define screening eligibility across 4 U.S. racial and ethnic groups.

DESIGN

Cohort study.

SETTING

United States, Lung Cancer Cohort Consortium.

PARTICIPANTS

641 830 participants aged 50 to 80 years with a smoking history from 12 U.S. cohorts, including 6390 Asian, 9781 Hispanic, 39 872 non-Hispanic Black, and 585 787 non-Hispanic White participants.

MEASUREMENTS: Calibration and discrimination were quantified for 16 lung cancer prediction models. Then, screening-related metrics were calculated after applying model thresholds to select the same number of eligible participants as the 2021 criteria from the U.S. Preventive Services Task Force (USPSTF-2021). These included eligibility, sensitivity, and efficiency measured as estimated number needed to screen (NNS; the ratio between participants and lung cancer cases) for each strategy or prediction model in each racial and ethnic group.

RESULTS

General patterns across the 16 models included substantial underestimation of lung cancer risk in non-Hispanic Black participants (expected-observed ratio < 0.75 for 11 of 16 models), lower discrimination in Asian participants than all other groups (13 of 16 models), and lower discrimination in non-Hispanic Black than non-Hispanic White participants (15 of 16 models). When a same-sized screening-eligible population as USPSTF-2021 (38.0%) was enforced, all risk-based strategies achieved better average estimated screening efficiency and reduced racial and ethnic differences in efficiency compared with USPSTF-2021. The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 (PLCOm2012) and Life Years gained From Screening-Computed Tomography model (LYFS-CT) performed best (mean estimated NNS, 36.5 [SD, 8.8] and 40.1 [SD, 8.2], respectively). However, no strategy could simultaneously optimize eligibility, sensitivity, and efficiency while also reducing racial and ethnic differences.

LIMITATION: Smaller sample for Asian and Hispanic participants.

CONCLUSION

To optimize efficiency and minimize its variation across racial and ethnic groups, risk-based strategies were superior to USPSTF criteria. Further optimization of prediction models for the diverse U.S. population is needed.

PRIMARY FUNDING SOURCE: U.S. National Cancer Institute, Lung Cancer Research Foundation, and Cancer Research UK.