Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom.

BACKGROUND

The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK.

METHODS

We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC).

RESULTS

Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%).

CONCLUSION

In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.