Abstract 3677: Prediction of oral cancer recurrence at post-surgery follow-up using fluorescence visualization
Bibliographic record
Abstract
Abstract Background: Recurrence is observed in up to 30% of surgically treated high-risk oral lesions (HRLs, severe dysplasia, carcinoma in situ and cancer) and is associated with poor prognosis. Reactive tissue change observed post-treatment often masks recurrence and makes early detection difficult. New advances using fluorescence visualization (FV) represent a promising approach to this problem that may facilitate early detection of disease recurrence. Objectives: 1) To identify FV alterations post-treatment of HRLs and 2) To determine whether a relationship exists between FV alterations and local recurrence. Methods: In the BC Oral Cancer Longitudinal Study, we have recruited ∼400 HRLs with surgical treatment as the primary modality. Patients eligible for this analysis included those that had an initial follow-up appointment within 6 months of surgery with at least 2 follow-up appointments within the first year of treatment, with each visit involving FV examination of the treatment site. Recurrence was defined as the presence of biopsy-proven HRLs. The ‘plateau’ of the FV during the follow-ups is defined as the change of FV measurement in width within ± 1 mm (superior-inferiorly) in various time intervals of 3, 6, 9, or 12 months. Results: A total of 198 patients were identified of which 24 (12%) had lesion recurrence at the previously treated site. There was no difference in gender, age, ethnicity, smoking habit, anatomical site, primary diagnoses, and follow-up time between the recurrence and non-recurrence groups. The duration of plateau was longer in non-recurrence group compared to those in recurrence group (P = 0.03). When we examined the duration ‘plateau’ at various intervals of follow-ups, among 166 patients/lesions with at least 9 months follow-ups, we found out that the presence of plateau was more frequent in the non-recurrence group than those recurrent case (P = 0.03). Using linear mixed effects and logistic regression analyses, there was a significant difference of the individual slopes between recurrence and non-recurrence group (P = 0.001), adjusted for the individual intercepts (i.e., the original FV width). Conclusion: The stability, i.e., timing and duration of the plateau, of the FV alteration during post-surgical follow-ups can be potentially used to predict local recurrence of HRLs. (Supported by Supported by grant R01 DE17013 from the National Institute of Dental and Craniofacial Research and grant CCSRI-20336 from Canadian Cancer Society Research Institute; Canadian Institute for Health Research and Michael Smith Foundation from Health Research) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3677. doi:10.1158/1538-7445.AM2011-3677
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".