Asian Lung Cancer Absolute Risk Models for lung cancer mortality based on China Kadoorie Biobank
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".