Incidence of non-pulmonary cancer and lung cancer by amount of emphysema and airway wall thickness: a community-based cohort
Bibliographic record
Abstract
There is limited knowledge about the prognostic value of quantitative computed tomography (CT) measures of emphysema and airway wall thickness in cancer.The aim of this study was to investigate if using CT to quantitatively assess the amount of emphysema and airway wall thickness independently predicts the subsequent incidence of non-pulmonary cancer and lung cancer.In the GenKOLS study of 2003-2005, 947 ever-smokers performed spirometry and underwent CT examination. The main predictors were the amount of emphysema measured by the percentage of low attenuation areas (%LAA) on CT and standardised measures of airway wall thickness (AWT-PI10). Cancer data from 2003-2013 were obtained from the Norwegian Cancer Register. The hazard ratio associated with emphysema and airway wall thickness was assessed using Cox proportional hazards regression for cancer diagnoses.During 10 years of follow-up, non-pulmonary cancer was diagnosed in 11% of the subjects with LAA <3%, in 19% of subjects with LAA 3-10%, and in 17% of subjects with LAA ≥10%. Corresponding numbers for lung cancer were 2%, 3% and 11%, respectively. After adjustment, the baseline amount of emphysema remained a significant predictor of the incidence of non-pulmonary cancer and lung cancer. Airway wall thickness did not predict cancer independently.This study offers a strong argument that emphysema is an independent risk factor for both non-pulmonary cancer and lung cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".