Optical clearing of melanoma<i>in vivo</i>: characterization by diffuse reflectance spectroscopy and optical coherence tomography
Bibliographic record
Abstract
Melanoma is the most aggressive type of skin cancer, with significant risk of fatality. Due to its pigmentation, light-based imaging and treatment techniques are limited to near the tumor surface, which is inadequate, for example, to evaluate the microvascular density that is associated with prognosis. White-light diffuse reflectance spectroscopy (DRS) and near-infrared optical coherence tomography (OCT) were used to evaluate the effect of a topically applied optical clearing agent (OCA) in melanoma in vivo and to image the microvascular network. DRS was performed using a contact fiber optic probe in the range from 450 to 650 nm. OCT imaging was performed using a swept-source system at 1310 nm. The OCT image data were processed using speckle variance and depth-encoded algorithms. Diffuse reflectance signals decreased with clearing, dropping by ∼ 90% after 45 min. OCT was able to image the microvasculature in the pigmented melanoma tissue with good spatial resolution up to a depth of ∼ 300 μm without the use of OCA; improved contrast resolution was achieved with optical clearing to a depth of ∼ 750 μm in tumor. These findings are relevant to potential clinical applications in melanoma, such as assessing prognosis and treatment responses. Optical clearing may also facilitate the use of light-based treatments such as photodynamic therapy.
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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) | 0.001 | 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".