BackgroundLung cancer is the leading cause of cancer mortality globally. Early detection through screening can markedly improve prognosis and prediction models can identify high-risk individuals for screening. However, most models have been developed in North American cohorts of smokers and much less is known about risk factors for never-smokers, which represent a growing proportion of lung cancers, particularly for Asian populations.MethodsBased on the China Kadoorie Biobank, a population-based prospective cohort study of 512,714 adults age 30-79 recruited between 2004-2008 with up to 12 years of follow-up, we built an Asian Lung Cancer Absolute Risk Model (ALARM) for lung cancer mortality using flexible parametric survival models, separately for ever- and never-smokers. Model performance was evaluated in a 25% held-out test set using the time-dependent area under the receiver operating characteristic curve (AUC) and by comparing the model-predicted and observed risks for agreement (i.e. calibration).ResultsPredictors assessed in the never-smoker lung cancer mortality model were age, sex, household income, lung function, history of emphysema/bronchitis, family history of cancer, personal cancer history, BMI, passive smoking, and indoor air pollution. The ever-smoker model additionally assessed smoking status (former vs. current), duration, intensity, and years since cessation. The 5-year AUC based on the hold-out test set for the never and ever-smoker models were 0.77 (95% CI: 0.73-0.81) and 0.81 (95% CI: 0.79-0.84), respectively. The maximum 5-year risk for never and ever smokers were 2.7% and 14.0%, respectively.ConclusionsThis study is among the first to develop and test risk models specifically for Asian population, separately for never (ALARM-NS) and ever-smokers (ALARM-ES). Our models identify Asian never- and ever-smokers at high-risk of death due to lung cancer with a high degree of accuracy, and may identify those with risks exceeding common eligibility thresholds who would likely benefit from lung cancer screening.