Jeffrey D Blume

Addressing algorithmic bias in lung cancer screening eligibility

Abstract Background The US Preventive Services Task Force (USPSTF) lung cancer screening eligibility guidelines and proposed risk models have been developed using data predominantly from White populations. Studies show that these eligibility strategies perform inconsistently across racially diverse populations, suggesting evidence of algorithmic bias. We assessed several lung cancer screening eligibility strategies and explored how algorithmic bias can be resolved to improve equity in eligibility. Methods Using the Southern Community Cohort Study, a large US study of predominantly Black/African American individuals, we evaluated the performance of 8 existing lung cancer screening eligibility strategies (USPSTF 2021; American Cancer Society 2023 recommendations; USPSTFSmokeDuration; Prostate, Lung, Colorectal and Ovarian 2012 risk prediction model [PLCOm2012]; PLCOm2012NoRace; PLCOm2012Update; Lung Cancer Risk Assessment Tool; and Lung Cancer Death Risk Assessment tool) and 2 new race-aware strategies proposed by our team (USPSTFRaceSpecific and PLCOm2012RaceSpecific). Results Among 52 667 adults (65% Black/African American, 31% White, 4% Multiracial/Other) with a smoking history, 1689 developed lung cancer over 15 years. Most screening strategies identified fewer Black/African American participants who developed lung cancer as eligible for screening vs their White counterparts (sensitivity for Black/African American individuals = 0.46-0.73 vs 0.72-0.80 for their White counterparts). Racial eligibility disparities were not resolved by removing race, removing the “years since quit” criterion, or using uniform risk thresholds. Replacing pack-years with smoking duration improved equity but overinflated the false-positive rate (0.71 for Black/African American persons vs 0.61 for White persons). Instead, race-aware approaches that tailored eligibility thresholds by race yielded the best sensitivity-specificity trade-off and minimized inequities (sensitivity = 0.71-0.73 for Black/African American persons vs 0.72-0.74 for White persons; false-positive rate = 0.49-0.50 for Black/African American persons vs 0.50-0.53 for White persons). Conclusion Our findings suggest that race-aware approaches are necessary to address algorithmic bias and ensure equitable opportunities for lung cancer screening.