{"doi":"10.1101/2022.04.22.22274185","title":"Asian Lung Cancer Absolute Risk Models for lung cancer mortality based on China Kadoorie Biobank","abstract":"Abstract 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.","journal":"medRxiv","year":2022,"id":308800,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9368,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":317247,"name":"Martin C. Tammemägi","orcid":"0000-0002-4989-5058","position":1,"is_corresponding":false},{"id":773896,"name":"Osvaldo Espin‐Garcia","orcid":"0000-0003-2052-2626","position":2,"is_corresponding":false},{"id":938004,"name":"Sanjeev Budhathoki","orcid":"0000-0003-1662-3109","position":3,"is_corresponding":false},{"id":251835,"name":"Geoffrey Liu","orcid":"0000-0002-2603-7296","position":4,"is_corresponding":false},{"id":261929,"name":"Rayjean J. Hung","orcid":"0000-0002-4486-7496","position":5,"is_corresponding":false},{"id":334597,"name":"Matthew T. Warkentin","orcid":"0000-0001-8730-3511","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:33:07.132912Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}