Quantitative monitoring of radiation induced skin toxicities in nude mice using optical biomarkers measured from diffuse optical reflectance spectroscopy
Bibliographic record
Abstract
Monitoring the onset of erythema following external beam radiation therapy has the potential to offer a means of managing skin toxicities via biological targeted agents - prior to full progression. However, current skin toxicity scoring systems are subjective and provide at best a qualitative evaluation. Here, we investigate the potential of diffuse optical spectroscopy (DOS) to provide quantitative metrics for scoring skin toxicity. A DOS fiberoptic reflectance probe was used to collect white light spectra at two probing depths using two short fixed source-collector pairs with optical probing depths sensitive to the skin surface. The acquired spectra were fit to a diffusion theory model of light transport in tissue to extract optical biomarkers (hemoglobin concentration, oxygen saturation, scattering power and slope) from superficial skin layers of nude mice, which were subjected to erythema inducing doses of ionizing radiation. A statistically significant increase in oxygenated hemoglobin (p < 0.0016) was found in the skin post-irradiation - confirming previous reports. More interesting, we observed for the first time that the spectral scattering parameters, A (p = 0.026) and k (p = 0.011), were an indicator of erythema at day 6 and could potentially serve as an early detection optical biomarker of skin toxicity. Our data suggests that reflectance DOS may be employed to provide quantitative assessment of skin toxicities following curative doses of external beam radiation.
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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".