Papillary and ductal patterns of mesonephric‐like adenocarcinomas are often overlooked: a retrospective revaluation of over 1000 endometrial carcinomas
Bibliographic record
Abstract
AIMS: Mesonephric-like adenocarcinoma (MLA) of the endometrium is often a diagnostic challenge, due to its morphological resemblance to other more common Müllerian neoplasms. This study aimed to retrospectively identify overlooked MLA in a large endometrial carcinoma cohort, using a combination of immunohistochemistry (IHC), morphology and KRAS sequencing. METHODS AND RESULTS: IHC was conducted on 1094 endometrial carcinomas, identifying 16 potential MLA cases based on GATA3+ and/or TTF1+ and ER- staining patterns, which subsequently underwent detailed histological review, KRAS sequencing and ProMisE molecular classification. Of the IHC screen-positive cases, one was positive for both GATA3 and TTF1, nine were positive for GATA3 only and six were positive for TTF1 only. All IHC screen-positive cases were POLE wild-type. All five tumours in the NSMP category showed morphological features of MLA, while the three MMRd and eight p53abn tumours did not show MLA morphology. The five cases diagnosed as MLA on review were all originally diagnosed as low-grade endometrioid adenocarcinoma probably because of rare morphological patterns, being predominantly papillary or ductal. Four of the five cases harboured a KRAS mutation. CONCLUSION: This study highlights the importance of a comprehensive diagnostic approach for accurately identifying endometrial MLA and for pathologists to be aware of papillary and ductal patterns in endometrial carcinoma assessment. Further exploration into the molecular landscape of MLA is essential for refining diagnostic criteria and developing targeted therapies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".