Can semi-automated image cytometry on induced sputum become a screening tool for lung cancer?
Bibliographic record
Abstract
The correlations between semi-automated sputum cytometry (ASC), conventional cytology and the final diagnosis was investigated in industrially-exposed workers. Slides of sputum samples from 201 former uranium miners with silicosis, 100 patients with asbestosis, 103 workers resected for lung cancer, and 200 controls (50% smokers), were stained using the Papanicolaou (Pap) method and the Feulgen reaction with thionin. Cytometry was performed using the Cyto-Savant automated system. Atypical nuclei were found in 72 of 404 patient samples, 327 samples were normal and five were inadequate for ASC analysis. Thirteen tumours (Pap IV, Pap V) and 11 cases of severe dysplasia were identified by cytology. Lung cancer was confirmed in 20 patients. Compared to the final diagnosis of lung cancer, the sensitivity of ASC was 75% (15 out of 20) and specificity 89.8% (520 out of 579). The results represent a diagnostic efficiency of 89.3%. The combination of ASC with cytology increased sensitivity to 80% (16 out of 20) without significant loss of specificity (89.7% or 523 out of 581). In this investigation of a limited number of patients with occupational radon or asbestos exposure, semi-automated sputum cytometry appears to be sensitive and reliable for the detection of malignant changes in the tracheobronchial mucosa. Together with conventional cytology, it would be reasonable to test the validity of the combined methods in a large-scale feasibility study of early lung cancer detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".