Quantitative assessment of oral microstructural and microvascular changes in late oral radiation toxicity, using noninvasive <i>in-vivo</i> optical coherence tomography
Bibliographic record
Abstract
Abstract Background and objectives: About half of the head and neck cancer patients treated with radiotherapy suffer from late radiation effects months to years after the treatment. The most common diagnosis and monitoring methods for such oral toxicities are based on surface examination of the oral tissue, which is subjective. Therefore, subsurface imaging and image quantification tools can be highly useful for monitoring these late effects as these approaches are more robust and objective. In this study, we demonstrate the ability of optical coherence tomography (OCT) technology and its newly developed quantitative imaging platform to reveal subsurface microstructural and microvascular changes in late oral radiation toxicity patients, not detectable by available clinical tools. Materials and methods: Fifteen patients exhibiting late oral radiation toxicity, and five healthy age-matched volunteers were imaged with OCT in a clinical pilot study. Image assessment methods, developed in-house, were used to extract four quantitative metrics of potential clinical importance from the acquired microstructural and microvascular oral OCT images. Results: The statistically significant differences in the patients compared to healthy volunteers were: lower epithelium to lamina propria thickness (indicating epithelial atrophy and/or fibrosis of lamina propria), smaller vessel diameter (indicating vessel lumen narrowing), and higher blood velocity. The observed in-vivo morphological changes correlated well with reported histology findings. No significant changes were observed in vessel tortuosity between the cohorts. Conclusion: The quantitative metrics extracted from the OCT images demonstrated significant microstructural and microvascular differences between the two cohorts. Potentially, OCT and its newly developed image analysis platform can be used as a noninvasive in-vivo subsurface tool for “shedding light” on late oral radiation toxicity, for example in palliative treatment efficacy monitoring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".