Autofluorescence-Guided Surveillance for Oral Cancer
Bibliographic record
Abstract
Early detection of oral premalignant lesions (OPL) and oral cancers (OC) is critical for improved survival. We evaluated if the addition of autofluorescence visualization (AFV) to conventional white-light examination (WLE) improved the ability to detect OPLs/OCs. Sixty high-risk patients, with suspicious oral lesions or recently diagnosed untreated OPLs/OCs, underwent sequential surveillance with WLE and AFV. Biopsies were obtained from all suspicious areas identified on both examinations (n = 189) and one normal-looking control area per person (n = 60). Sensitivity, specificity, and predictive values were calculated for WLE, AFV, and WLE + AFV. Estimates were calculated separately for lesions classified by histopathologic grades as low-grade lesions, high-grade lesions (HGL), and OCs. Sequential surveillance with WLE + AFV provided a greater sensitivity than WLE in detecting low-grade lesions (75% versus 44%), HGLs (100% versus 71%), and OCs (100% versus 80%). The specificity in detecting OPLs/OCs decreased from 70% with WLE to 38% with WLE + AFV. Thirteen of the 76 additional biopsies (17%) obtained based on AFV findings were HGLs/OCs. Five patients (8%) were diagnosed with a HGL/OC only because of the addition of AFV to WLE. In seven patients, additional HGL/OC foci or wider OC margins were detected on AFV. Additionally, AFV aided in the detection of metachronous HGL/OC in 6 of 26 patients (23%) with a history of previously treated head and neck cancer. Overall, the addition of AFV to WLE improved the ability to detect HGLs/OCs. In spite of the lower specificity, AFV + WLE can be a highly sensitive first-line surveillance tool for detecting OPLs/OCs in high-risk patients.
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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 | 0.000 | 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) | 0.000 | 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 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".