<i>In vivo</i>Optical Coherence Tomography Imaging of Preinvasive Bronchial Lesions
Bibliographic record
Abstract
PURPOSE: Optical coherence tomography (OCT) is an optical imaging method that can visualize cellular and extracellular structures at and below tissue surface. The objective of the study was to determine if OCT could characterize preneoplastic changes in the bronchial epithelium identified by autofluorescence bronchoscopy. EXPERIMENTAL DESIGN: A 1.5-mm fiberoptic probe was inserted via a bronchoscope into the airways of 138 volunteer heavy smokers participating in a chemoprevention trial and 10 patients with lung cancer to evaluate areas that were found to be normal or abnormal on autofluorescence bronchoscopy. Radial scanning of the airways was done to generate OCT images in real time. Following OCT imaging, the same sites were biopsied for pathologic correlation. RESULTS: A total of 281 OCT images and the corresponding bronchial biopsies were obtained. The histopathology of these areas includes 145 normal/hyperplasia, 61 metaplasia, 39 mild dysplasia, 10 moderate dysplasia, 6 severe dysplasia, 7 carcinoma in situ, and 13 invasive carcinomas. Quantitative measurement of the epithelial thickness showed that invasive carcinoma was significantly different than carcinoma in situ (P=0.004) and dysplasia was significantly different than metaplasia or hyperplasia (P=0.002). In addition, nuclei of the cells corresponding to histologic results became more discernible in lesions that were moderate dysplasia or worse compared with lower-grade lesions. CONCLUSION: Preliminary data suggest that autofluorescence bronchoscopy-guided OCT imaging of bronchial lesions is technically feasible. OCT may be a promising nonbiopsy tool for in vivo imaging of preneoplastic bronchial lesions to study their natural history and the effect of chemopreventive intervention.
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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 | 0.000 | 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) | 0.001 | 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".