Ovarian Endometrioid Carcinoma Misdiagnosed as Mucinous Carcinoma: An Underrecognized Problem
Bibliographic record
Abstract
Primary mucinous carcinoma of the ovary is uncommon, and while numerous studies have focused on improving our ability to distinguish these tumors from gastrointestinal metastases, recent data suggest that up to one fifth are still misdiagnosed with a previously underrecognized culprit: endometrioid carcinoma. Using an index case of an ovarian endometrioid carcinoma with mucinous differentiation masquerading as a mucinous carcinoma, we sought to identify the most efficient biomarker combination that could distinguish these 2 histotypes. Eight immunohistochemical markers were assessed on tissue microarrays from 183 endometrioid carcinomas, 77 mucinous carcinomas, and 72 mucinous borderline tumors. Recursive partitioning revealed a simple 2-marker panel consisting of PR and vimentin. The combination of PR absence and vimentin absence could predict mucinous tumors with a sensitivity of 95.1%, a specificity of 96.7%, and an overall accuracy of 96.0%. Additional marker combinations did not improve accuracy. The 5-yr ovarian cancer-specific survival for mucinous carcinoma was significantly worse than endometrioid carcinoma (70% vs. 86%, respectively, P=0.02). Our proposed 2-marker algorithm allows diagnostic distinction between mucinous and endometrioid ovarian carcinomas when morphology is not straightforward. Given key differences in the underlying biology and clinical behavior of these 2 histotypes, improved diagnostic precision is essential for guiding appropriate management and treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".