Time-Resolved Fluorescence Spectroscopy as a Diagnostic Technique of Oral Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To investigate the benefit of using time-resolved, laser-induced fluorescence spectroscopy for diagnosing malignant and premalignant lesions of the oral cavity. DESIGN: The carcinogen 7,12-dimethylbenz[a]anthracene (DMBA) was applied to 1 cheek pouch of 19 hamsters. The contralateral pouch and the cheek pouches of 3 hamsters without DMBA exposure served as controls. SETTING: University of California, Davis. PARTICIPANTS: Twenty-two golden/Syrian hamsters. INTERVENTION: A nitrogen pulse laser was used to induce tissue autofluorescence between the wavelengths of 360 and 650 nm. MAIN OUTCOME MEASURES: Spectral intensities and time-domain measurements were obtained and compared with the histopathologic findings at each corresponding site. RESULTS: Spectral intensities and lifetime values at 3 spectral bands (SBs; SB1 = 380 +/- 10 nm; SB2 = 460 +/- 10 nm, and SB3 = 635 +/- 10 nm) allowed for discrimination among healthy epithelium, dysplasia, carcinoma in situ, and invasive carcinoma. The lifetime values at SB2 were the most important when distinguishing the lesions using only time-resolved parameters. An algorithm combining spectral fluorescence parameters derived from both spectral and time-domain parameters (peak intensities, average fluorescence lifetimes, and the Laguerre coefficient [zero-order]) for healthy epithelium, dysplasia, carcinoma in situ, and invasive carcinoma provided the best diagnostic discrimination, with 100%, 100%, 69.2%, and 76.5% sensitivity and 100%, 92.2%, 97.1%, and 96.2% specificity, respectively. CONCLUSIONS: The addition of time-resolved fluorescence-derived parameters significantly improves the capability of fluorescence spectroscopy-based diagnostics in the hamster buccal pouch. This technique provides a potential noninvasive diagnostic instrument for head and neck cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".