Combining Autofluorescence and Narrow Band Imaging With Image Analysis in the Evaluation of Preneoplastic Lesions in the Bronchus and Larynx
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Autofluorescence (AF) techniques improve the diagnostic yield of white light inspection for preneoplastic lesions in the bronchus and head and neck region. Although highly sensitive, AF has poor specificity, particularly in situations where there have been earlier biopsies or treatments such as radiotherapy. Narrow band imaging (NBI) is a newer imaging technique that enhances the early abnormal angiogenesis seen in preneoplastic lesions. NBI has higher specificity when compared with AF. We aimed to combine these imaging modalities, using AF as an effective screening tool and NBI to confirm AF findings. We also used computer-assisted image analysis techniques to give objective confirmation to our visual inspection. METHODS: Three patients were selected for image analysis of their NBI images using the L*a*b* color scale in manually drawn regions of interest of biopsy-confirmed areas. Each case compared pathology with a different benign condition: normal tissue, postbiopsy effect, and postradiation therapy change. Patients had white light followed by AF inspection. Abnormal areas of AF were cross-examined with NBI. RESULTS: NBI clearly showed dysplasia and carcinoma in situ. It also confirmed abnormal fluorescence because of earlier biopsies and radiation therapy. Analysis of the L*a*b* color space scale in each case showed segmentation between pathology and the benign tissue. CONCLUSIONS: There may be additive and discriminatory benefits of NBI after AF inspection. Further study with computer-assisted color segmentation techniques and image analysis is required before optical diagnosis can become a reality in bronchoscopic techniques in the future.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".