Correlating optical coherence tomography images with dose distribution in late oral radiation toxicity patients
Bibliographic record
Abstract
Abstract Background and objective: Late oral radiation toxicity occurs in about half of the patients who undergo head-and-neck radiotherapy, reducing the quality of life drastically. The total delivered radiation dose has been shown to be one of the predictors of these late complications. To demonstrate this, the studies carried out so far have used either visual observation together with symptom-based scoring systems or histology to evaluate the tissue response. However, the former lacks imaging information on tissue subsurface and the latter is invasive and exposes the patients to additional risks. Therefore, there is a need for a non-hazardous, non-invasive subsurface monitoring tool that can provide more objective information on dose-dependent response of normal oral tissue to radiation. Materials and methods: To address this unmet clinical need, optical coherence tomography (OCT) was used. A clinical study was conducted on 14 late oral radiation toxicity patients and 5 age-matched healthy volunteers. OCT structural images were acquired from different oral regions in both cohorts and were then de-speckled in order to provide a better visualization of the subsurface layers and features. The alterations in patients’ de-speckled OCT images compared to the healthy cohort are reviewed, and potential correlations between the total dose to specific regions and the abnormal features observed in the OCT images of the same sites are discussed. Results: OCT images were acquired from 32 sites in 14 patients, 15 of these sites belonged to the regions which received >50 Gy radiation dose and the rest to the region which was irradiated to <50 Gy. The de-speckled OCT images from the former group showed major differences, such as total layer disruption, compared to the images of healthy oral tissue. The de-speckled OCT images from all but one of the regions which received <50 Gy showed normal features and layer definition compared to healthy oral OCT images. Same results were observed in an intra-patient comparison of the ipsilateral (dose >70 Gy) and contralateral (dose <25 Gy) soft palate of one of the patients, suggesting the results are independent of anatomical differences between individuals. Conclusion: This preliminary clinical study showed the ability of OCT to differentiate subsurface features in the oral regions that received high radiation dose (>50 Gy) compared to healthy oral tissue. These results can be used to design a prospective study to monitor oral subsurface changes periodically (e.g., every 6 months) following radiotherapy, to further understand the mechanism of late radiation toxicity and its relation to dose.
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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 | 0.001 | 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".