Abstract 2901: Visualization and delineation of high-risk fields in the oral cavity
Bibliographic record
Abstract
Abstract Oral cancer is a substantial, though often unrecognized issue globally, with close to 300,000 new cases reported annually. The disease represents a management conundrum: this is a cancer site that is easily examined; yet more that 40% of oral cancers are diagnosed at a late stage when the chance of death is high and treatment can be disfiguring and devastating. Visualization of high-risk fields can be improved by application of contrast agents, such as toluidine blue (TB) or through use of devices that measure alteration to tissue optics, such as fluorescence visualization (FV) both of which could facilitate assessment of abnormalities. This study's objective was to evaluate FV, within a high-risk clinic, to look for associations between loss of autofluorescence (FVL) and alterations to clinical, histological and molecular features and to determine its ability to detect high-risk oral premalignant fields and cancer. Methods: The study involved 170 patients, with 192 oral lesions (64 cancers, 28 severe dysplasia, 66 low-grade (mild/moderate) dysplasia and 34 nondysplasia), being followed in the ongoing Oral Cancer Prediction Longitudinal Study. Four categories of data were collected: 1) demographic and habit information (age, gender, ethnicity and tobacco habits); 2) lesion histology; 3) clinicopathological features at time of biopsy (lesion size, site, appearance, toluidine blue (TB) staining and FV status); and 4) molecular risk patterns of the lesions (loss of heterozygosity, LOH). Results: Demographics and smoking habit were not associated with FV status. Clinicopathological features of the lesion, appearance (P<0.001) and presence at a high-risk site (P=0.018) were significantly associated with FVL. FV status was strongly associated with severity of histology (P<0.001) with 96% of severe dysplasia, and 97% cancer displaying FVL. FVL lesions showed a significantly higher frequency of loss at 3 molecular risk sites, 3p14 (P=0.050), 9p21 (P=0.021), and 17p11-13 (P=0.05), as well as loss at 2 or more arms (P=0.036). Within low-grade dysplasia and nondysplasias, FV status was not associated with clinical features and LOH at 3p, 9p and 17p (although there was a nonspecific trend, P =0.076, 0.054. 0.077, respectively) but was associated with the presence of LOH on multiple (>2) arms (P=0.009). TB positivity was found to be highly associated with FVL (P<0.001). To date, 7 premalignant lesions have progressed to a high-grade lesion or SCC and all were FVL at time of low-grade dysplasia biopsy (FVL and progression, P=0.047). Six of the 7 progressing lesions were TB+. Conclusion: FV was found to be a very useful adjunctive tool when used by experienced clinicians in high-risk clinics. Integrating TB and FV results may aid in the detection of low-grade lesions at risk of progression. Further study of FV in low-grade and nondysplasia with a larger sample size is required. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2901.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".