Incremental Value of Pulmonary Function and Sputum DNA Image Cytometry in Lung Cancer Risk Prediction
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide. Accurate prediction of lung cancer risk is of value for individuals, clinicians, and researchers. The aims of this study were to characterize the associations between pulmonary function and sputum DNA image cytometry (SDIC) and lung cancer, and their contributions to risk prediction. During 1990 to 2007, 2,596 high-risk individuals were enrolled and followed prospectively for development of lung cancer (n = 139; median follow-up 7.7 years) in trials at the British Columbia Cancer Agency. At baseline, an epidemiologic questionnaire was administered, sputum was collected for aneuploidy measurement and spirometry was obtained. Multivariable logistic models were prepared including known lung cancer predictors (model 1), that additionally included percent-expected-forced expiratory volume in 1 second [forced expiratory volume in 1 second (FEV(1)%), model 2], and that additionally included SDIC (model 3). Prediction was assessed by evaluating discrimination (receiver operator characteristic area under the curve (ROC AUC)) and calibration. Net reclassification indices (NRI) were calculated with cutoff points for 8-year risks identifying low, intermediate, and high risk at 1.5% and 3%. Lung cancer risk increased with decline in FEV(1)%, but did so more for men than for women (interaction P < 0.001). SDIC demonstrated a dose-response with lung cancer (P = 0.022). The ROC AUCs for models 1, 2, and 3 were 0.718 (95% CI: 0.671-0.765), 0.767 (95% CI: 0.725-0.809), and 0.773 (95% CI: 0.732-0.815), respectively. Model 2 versus 1 had a NRI of 12.6% (P < 0.0001) and model 3 versus 2 had a NRI of 3.1% (P = 0.059). Spirometry and SDIC data substantially and minimally improved lung cancer prediction, respectively.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".