Napsin-A and AMACR are Superior to HNF-1β in Distinguishing Between Mesonephric Carcinomas and Clear Cell Carcinomas of the Gynecologic Tract
Bibliographic record
Abstract
Mesonephric carcinoma is a rare gynecologic neoplasm commonly mistaken for clear cell carcinoma, because of their overlapping morphologic features. Both tumors are negative for estrogen receptor and p16, magnifying this diagnostic dilemma. Recently, hepatocyte nuclear factor-1 beta (HNF-1β), a marker for clear cell carcinoma, has also been shown to be positive in mesonephric carcinomas. Other more recent markers for clear cell carcinoma, however, such as Napsin-A and alpha-methylacyl-CoA racemase (AMACR), have not yet been studied in mesonephric carcinomas. Here we examine HNF-1β, AMACR, and Napsin-A immunohistochemistry in 18 mesonephric and 55 endometrial/cervical clear cell carcinomas. HNF-1β was considered positive if nuclear staining was present in ≥70% of cells and at least moderate intensity; for Napsin-A and AMACR, any cytoplasmic staining was considered positive (≥1%). H-scores were determined by multiplying the intensity score by proportion score. HNF-1β was positive in a substantial portion of mesonephric carcinomas (9/18, 50%; H-score 98) and clear cell carcinomas (34/55, 62%; H-score 163) and did not distinguish between the 2 entities (specificity, 50%; P-value of H-score=0.08). Napsin-A and AMACR expression was significantly higher in clear cell [43/55 (78%) and 41/55 (75%), respectively] than mesonephric carcinomas [4/18 (22%) and 4/18 (22%) respectively], and helpful in this differential (specificity: 78% and 78%; P<0.05 for both). When Napsin-A and AMACR staining were seen in mesonephric carcinomas, staining was focal (≤5%), whereas staining in clear cell carcinomas was patchy/diffuse. In summary, Napsin-A and AMACR are helpful in distinguishing mesonephric carcinomas from clear cell carcinomas of the female genital tract, but HNF-1β is not.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".