Detection and localization of early lung cancer by fluorescence bronchoscopy
Bibliographic record
Abstract
BACKGROUND Curative therapy is available for patients with Stage 0 lung carcinoma, with a >90% 5-year survival rate. Promising chemopreventive agents also are under investigation currently to reduce the risk of lung carcinoma in high risk populations. However, preinvasive bronchial lesions (moderate to severe dysplasia and carcinoma in situ) are very small and thin. They are difficult to localize by conventional white-light bronchoscopy. Fluorescence bronchoscopy is a new diagnostic tool for the detection of these preinvasive lesions. METHODS The data on the use of fluorescence bronchoscopy to detect and localize preinvasive lesions in current heavy smokers and in former smokers at the British Columbia Cancer Agency as well as the worldwide experience cited in MEDLINE, Index Medicus, and Deutsches Institut fur Medizinische Dokumentation und Information (Cologne, Germany) comparing white-light and fluorescence bronchoscopy using the lung imaging fluorescence endoscope (LIFE)-Lung device (Xillix Technologies Corp., Richmond, British Columbia, Canada) were reviewed. RESULTS Among current heavy smokers and former smokers with sputum atypia, the prevalence of carcinoma in situ was 1.6%. Moderate or severe dysplasia was found in another 19%. The preinvasive lesions were found to be small: 55% measured ≤1.5 mm in greatest dimension. Over 1000 cases have been reported in the literature between 1994 and 1999. Overall, 40% of the preinvasive lesions were detected by white-light bronchoscopy alone. The addition of fluorescence bronchoscopy increased the detection rate to an average of 80%. CONCLUSIONS Preinvasive lesions, especially dysplastic lesions, are small. They are difficult to detect and localize by white-light bronchoscopy. Fluorescence bronchoscopy improves the detection rate. It is an important part of the armamentarium in the overall management of early lung cancer. Cancer 2000;89:2468–73. © 2000 American Cancer Society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".