Clinicopathological Features Associated with Fluorescence Alteration: Analysis of 108 Oral Malignant and Potentially Malignant Lesions
Bibliographic record
Abstract
Background: Several noninvasive diagnostic tools have been developed to aid the early detection of oral cancer and for evaluation before definitive biopsy. Among these, devices evaluating a tissues autofluorescence (AF) are emerging. In particular, the most well known of these is the VELscope® system (LED Medical Diagnostics, Inc., Barnaby, Canada), which emits a light of 400–460 nm. This study has been developed to describe the most relevant clinicopathological features associated with AF alterations in a set of patients with oral squamous cell carcinoma and potentially malignant disorders (PMDs). Materials and methods: Overall, 108 lesions from 60 patients with clinical diagnoses of potentially malignant oral disorders and carcinomas were included in the study. For each case, the following variables were recorded and compared with the AF pattern: (1) clinical appearance (white, red, and white/red); (2) histological diagnosis (no dysplasia, mild/moderate dysplasia, severe dysplasia/in situ carcinoma, invasive carcinoma, and verrucous carcinoma); and (3) clinicopathological diagnosis. Binomial logistic regression was performed to investigate whether clinical appearance and/or histological diagnosis were significant in determining the degree of AF. Results: Among the white lesions, 66% resulted in hyper-fluorescence, whereas the red lesions appeared hypo-fluorescent in 95.2% of cases. The AF was altered (both hypo-fluorescent and hyper-fluorescent) in 36% of lesions without dysplasia; in 75.9% of lesions with mild or moderate dysplasia and in the totality of the in situ, invasive, and verrucous carcinomas (p < 0.0001). With regard to the binomial logistic regression, variables were separately considered and both were extremely significant in determining the degree of AF. Conclusions: Promising evidence for the use of AF as an adjunctive tool to conventional oral examinations (COEs) has been demonstrated. However, although the sensitivity of AF examination associated with COE is very high, both the literature and this study agree to indicate a low specificity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".