Evaluation of Radiation-Induced Oral Mucositis by Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: Optical coherence tomography (OCT) imaging was evaluated to determine if radiation-induced mucosal damage could be noninvasively monitored in real time and correlated with histopathologic findings. EXPERIMENTAL DESIGN: Female C3H mice, ages 7 to 9 weeks, four per group, were immobilized in a custom-made Lucite jig and received 0, 15, 22.5, and 25 Gy in a single fraction to their oral cavity. OCT images were acquired of proximal, middle, and distal aspects of the dorsum of the tongue on days 0, 1, 3, 5, and 7 post-irradiation. Animals were sacrificed on day 7 and samples taken for histologic evaluation. OCT images were visually examined and also quantified by image analysis and compared with histologic findings. RESULTS: Tongues removed 7 days post-irradiation showed no visible damage; however, upon staining with toluidine blue, ulcers at the base of the tongue became visible (100% for 25 Gy, 75% after 22.5 Gy, and 0% after 15 Gy). Visual inspection of OCT images qualitatively compared with histologic findings and quantitative image analysis of the OCT images (effective light penetration depth) revealed significant changes 7 days post-irradiation compared with unirradiated controls for the base of the tongue. CONCLUSIONS: OCT allows for direct noninvasive real-time acquisition of digitally archivable images of oral mucosa and can detect radiation-induced changes in the mucosa before visual manifestation. OCT may be a useful technique to quantify subclinical radiation-induced mucosal injury in experimental chemoradiation clinical trials.
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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 | 0.000 |
| 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) | 0.001 | 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".