Assessing eligibility for lung cancer screening: Parsimonious multi-country ensemble machine learning models for lung cancer prediction

BackgroundEnsemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models whilst maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. MethodsFor model development, we used data from 216,714 ever-smokers in the UK Biobank prospective cohort and 26,616 high-risk ever-smokers in the control arm of the US National Lung Screening randomised controlled trial. We externally validated our models amongst the 49,593 participants in the chest radiography arm and amongst all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian Screening Trial (PLCO). Models were developed to predict the risk of two outcomes within five years from baseline: diagnosis of lung cancer, and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. ResultsModels predicting lung cancer death (UCL-D) and incidence (UCL-I) using three variables - age, smoking duration, and pack-years - achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783-0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95-1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771-0.802), an E/O ratio of 1.0 (0.92-1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. ConclusionsWe present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.