Malignant Melanoma of Vulva and Vagina
Bibliographic record
Abstract
OBJECTIVES: The aim of this work was to determine molecular characteristics and specifically, the frequency of BRAF, C-KIT, and NRAS mutations in vulvar and vaginal melanomas. METHODS: A retrospective review of all cases of vulvar and vaginal melanoma between 2002 and 2013 was performed. We reviewed the clinical and histological characteristics of all cases and performed genotyping studies on cases that had tissue available for the study, using next-generation sequencing. RESULTS: We identified 33 vulvar and 11 vaginal melanomas in women with mean ages 58 and 61 years, respectively. Next-generation sequencing analysis on 20 cases (15 vulvar and 5 vaginal) identified a BRAF mutation in 7.6%, C-KIT mutation in 27.6%, NRAS mutation in 27.6%, and TP53 mutation in 7.6% of the vulvar cases. We detected only a single TP53 mutation in the vaginal cases. We did not identify any statistically significant relationship between the mutation status and patients' outcome, depth of invasion, ulceration, stage at presentation, or lymph node metastasis. CONCLUSIONS: BRAF mutations are infrequent, whereas C-KIT and NRAS mutations are seen with higher frequency in vulvar melanomas than melanomas of other sites. These mutations can be considered as potential therapeutic targets in patients harboring them. Further studies are necessary to increase our understanding of mutational events occurring in melanoma of the lower female genital tract and their relationship with clinical parameters/outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".