Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

Background Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while 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. Methods and findings For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 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. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 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 (95% CI: 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. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. Conclusions We 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. Author summary Why was this study done? Screening and disease prevention programmes are increasingly bespoke; however, their simultaneous delivery at a population-scale presents considerable challenges. Lung cancer is the most common cause of cancer death worldwide, with poor survival in the absence of early detection. Screening for lung cancer among those at high-risk could reduce lung cancer-specific mortality by 20% to 24% among those screened, but the ideal way to determine if someone is high-risk remains uncertain and existing approaches are resource intensive. What did the researchers do and find? We used data from the UK Biobank and US National Lung Screening Trial to develop novel, parsimonious models, to simplify the prediction of lung cancer risk and selection to lung cancer screening programmes. Using ensemble machine learning and 3 predictors-age, smoking duration, and pack-years-we found our models achieved or exceeded parity in performance with leading comparators despite requiring one-third of the variables. Our models were externally validated in the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and benchmarked against models that are either in use or have performed strongly in previous analyses. What do these findings mean? Risk assessment for lung cancer screening can be simplified without reducing performance, potentially improving the uptake and effectiveness of national lung cancer screening programmes, and therefore contributing to reducing deaths from lung cancer. Future research should focus on the application of this modelling approach to other conditions such as cardiovascular disease, diabetes, and chronic kidney disease to support the implementation at scale of multiple concurrent risk-stratified prevention and early detection programmes for major causes of morbidity and mortality. This study has key limitations as it is based on past data from the US and UK, so prospective evaluation in different countries and regions should be considered.

Automated summary

Risk-based screening for lung cancer is being considered in several countries, and ensemble machine learning models have been developed and validated to determine eligibility for this screening program. These models simplify the prediction of lung cancer risk using only three predictors, showing performance comparable to existing models but with fewer variables. External validation demonstrated the potential for simplified risk assessment in lung cancer screening, which could improve the uptake and effectiveness of national screening programs and contribute to reducing lung cancer deaths. Future research should focus on applying this approach to other conditions and evaluating it in different countries and regions.