In-vivo longitudinal imaging of microvascular changes in irradiated oral mucosa of radiotherapy cancer patients using optical coherence tomography
Bibliographic record
Abstract
Mucositis is the limiting toxicity of radio(chemo)therapy of head and neck cancer. Diagnostics, prophylaxis and correction of this condition demand new accurate and objective approaches. Here we report on an in vivo longitudinal monitoring of the oral mucosa dynamics in 25 patients during the course of radiotherapy of oropharyngeal and nasopharyngeal cancer using multifunctional optical coherence tomography (OCT). A spectral domain OCT system with a specially-designed oral imaging probe was used. Microvasculature visualization was based on temporal speckle variations of the full complex signal evaluated by high-pass filtering of 3D data along the slow scan axis. Angiographic image quantification demonstrated an increase of the vascular density and total length of capillary-like-vessels before visual signs or clinical symptoms of mucositis occur. Especially significant microvascular changes compared to their initial levels occurred when grade two and three mucositis developed. Further, microvascular reaction was seen to be dose-level dependent. OCT monitoring in radiotherapy offers a non-invasive, convenient, label-free quantifiable structural and functional volumetric imaging method suitable for longitudinal human patient studies, furnishing fundamental radiobiological insights and potentially providing useful feedback data to enable adaptive radiotherapy (ART).
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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 | 0.000 | 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".