Lung Cancer Screening
Bibliographic record
Abstract
Thoracic computed tomography (CT) is a sensitive method for detecting early lung cancer but has a high false-positive rate and is not sensitive for detecting central preinvasive and microinvasive cancer. Our hypothesis was that automated quantitative image cytometry (AQC) of sputum cells as the first screening method may improve detection rate by identifying individuals at highest risk for lung cancer. A total of 561 volunteer current or former smokers 50 years of age or older, with a smoking history of more than or equal to 30 pack/years, were studied. Among these, 423 were found to have sputum atypia defined as five cells or more with abnormal DNA content using AQC. Noncalcified pulmonary nodules were found in 46% (259/561). Of the 14 detected cancers, 13 were detected in subjects with sputum atypia-nine by CT and four carcinoma in situ/microinvasive cancers by autofluorescence bronchoscopy. One cancer was detected by CT alone. AQC of sputum cells improved the detection rate of lung cancer from 1.8 to 3.1%. CT scan alone would have missed 29% of the cancers. This screening paradigm shift has the additional potential of reducing the number of initial CT scans by at least 25% with further savings in follow-up investigations and treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".