Non-invasive dynamic assessment of conjunctival melanomas by photoacoustic imaging
Bibliographic record
Abstract
This study describes non-invasive photoacoustic imaging to detect and monitor the growth of conjunctival melanomas in vivo. Conjunctival melanomas were induced by injection of melanotic B16F10 cells into the subconjunctival space in syngeneic albino C57BL/6 mice. Non-invasive in vivo photoacoustic tomography was performed before, and after tumor induction up to 2 weeks. Spectral unmixing was performed to determine the location and to assess the distribution of melanin. The melanin photoacoustic signal intensity was quantified from the tumor-bearing and control eyes at all timepoints. For postmortem validation, total tumor and melanotic tumor volumes were measured using H&E stained tumor sections and were compared to in vivo photoacoustic imaging measurements. Photoacoustic imaging non-invasively detected eyes bearing conjunctival tumors of varying sizes. The melanin signal was detected as early as immediately following injection of melanotic tumor cells. Changes in tumor size over time were assessed with changes in the volume and intensity of the melanin signal. Four growing tumors and one regressing tumor were observed. Three tumors without significant change in signal intensity over time were observed, showing variable growth. Photoacoustic melanin signal on the last day of in vivo imaging correlated with postmortem total tumor volume (R 2 = 0.81) and melanotic tumor volume (R 2 = 0.80). The results of our study show that actively growing conjunctival melanomas can be quantified in a non-invasive manner using in vivo photoacoustic tomography. The photoacoustic melanin signal intensity correlated with total and melanotic tumor volume. This novel in vivo imaging platform may help to assess new treatment modalities to manage ocular tumors.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".