Napsin A, Hepatocyte Nuclear Factor-1-Beta (HNF-1β), Estrogen and Progesterone Receptors Expression in Arias-Stella Reaction
Bibliographic record
Abstract
BACKGROUND: The Arias-Stella reaction (ASR) can mimic endometrial clear cell carcinoma (ECCC) in small biopsies, especially when drug or pregnancy history is unknown. A panel of immunohistochemical markers comprising napsin A, hepatocyte nuclear factor-1-beta (HNF-1β), estrogen and progesterone receptors (ER, PR) has been found useful in confirming a diagnosis of ECCC. However, the detailed characterization of how expression of this combination of markers in the ECCC mimics ASR has yet to be thoroughly evaluated. DESIGN: The frequency and extent of napsin A, HNF-1β, ER, and PR expression in ASR were assessed in a large series. For napsin A, any cytoplasmic staining was considered positive while only nuclear staining was deemed to be positive for HNF-1β, ER, and PR. Immunohistochemical histoscores based on the intensity and extent of staining were calculated. RESULTS: Forty cases were gestational and 10 were nongestational ASR. In 19 (38%), the reaction was extensive and involved >50% of the glands. A stromal decidual change was found in 31 (77.5%) of the gestational and 3 (30%) of the nongestational cases. Napsin A was positive in all gestational and 8 of 10 (80%) nongestational ASR. All ASR showed HNF-1β expression. ER expression was reduced in 37 (92.5%) and lost in 3 (7.5%) gestational ASR, and reduced in 9 (90%) and lost in 1 (10%) of nongestational ASR. None of the ASR in our series expressed PR. CONCLUSIONS: Naspin A and HNF-1β were frequently expressed in both gestational and nongestational ASR, and ER expression was usually either reduced or loss. Interpretation of these markers in small biopsies containing atypical clear cells should be made with caution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".