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Record W4389608716 · doi:10.1097/pgp.0000000000001004

PRAME Immunohistochemistry for Distinguishing Vulvar and Vaginal Melanoma From Benign Melanocytic Nevi

2023· article· en· W4389608716 on OpenAlexaff
Spencer D. Martin, Karina C. Martin, C. Blake Gilks, Richard I. Crawford, Lien Hoang

Bibliographic record

VenueInternational Journal of Gynecological Pathology · 2023
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsImmunohistochemistryMelanomaMedicineVulvaPathologyHMB-45DermatologyCancer research

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.316
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Gynecological PathologySame topicCutaneous Melanoma Detection and ManagementFrench-language works237,207