Asian Lung Cancer Absolute Risk Models for lung cancer mortality based on China Kadoorie Biobank

Background Lung 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 risk-based 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. Methods Based on the China Kadoorie Biobank, a population-based prospective cohort study of 512,639 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, accounting for competing risks of all-other-cause mortality. Model performance was evaluated in a 25% hold-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 model calibration. Results Predictors 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, and intensity. The 5-year AUC based on the hold-out test set for the never and ever-smoker models were 0.77 (95% CI: 0.73-0.80) and 0.81 (95% CI: 0.79-0.84), respectively. The maximum 5-year risk for never and ever smokers were 2.6% and 12.7%, respectively. Conclusions This study is among the first to develop and test risk models specifically for Asian populations, 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by Canadian Institutes of Health Research (FDN 167273) and the National Institutes of Health (U19 CA203654). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics committee of the Mount Sinai Hospital gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data is available from the China Kadoorie Biobank () upon approval of the CKB Data Access Committee.