PRAME Immunohistochemistry for Distinguishing Vulvar and Vaginal Melanoma From Benign Melanocytic Nevi
Bibliographic record
Abstract
Vulvovaginal melanoma (VVM) is a rare but deadly disease, accounting for 5% of all vulvar malignancies, with a 5-yr survival rate of only 47% for all stages of the disease. VVM is a distinct subset of melanoma, with a unique genomic profile and underlying pathogenesis unassociated with sun exposure. Distinguishing these rare malignancies from very common pigmented lesions of the vulva and vagina is challenging as histologic features often overlap between entities. PReferentially expressed Antigen in MElanoma (PRAME) is a melanoma-associated protein, and immunohistochemistry (IHC) for PRAME distinguishes cutaneous, oral mucosal, and retinal melanoma from atypical nevi. Given the biological differences between VVM and cutaneous melanoma, the utility of PRAME IHC for the diagnosis of VVM is unknown. We accrued a cohort of 20 VVM and 21 benign vulvar melanocytic nevi. We found that nuclear PRAME IHC staining with 4+ intensity was present in 85% of the VVM and 0% of the nevi. With the assistance of PRAME IHC, we found evidence of close or positive margin involvement in 3 of 10 cases where margins were originally diagnosed as negative for melanoma in situ. Our study is the first to assess PRAME IHC in a cohort of VVM cases and provides confidence for using PRAME IHC to assist with diagnosis and margin assessment in this rare disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".