A 5‐marker immunohistochemical panel of <scp>CK17</scp> , <scp>MEP1A</scp> , <scp>PAX8</scp> , <scp>SMAD4</scp> , and <scp>CDX2</scp> to distinguish ovarian mucinous carcinoma from metastatic pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
AIMS: Metastatic pancreatic adenocarcinoma (PDAC), albeit uncommon, may involve the ovary, and distinction from primary ovarian mucinous tumours (OMT) poses a diagnostic challenge. Our aim was to develop an ancillary immunohistochemical (IHC) panel to aid in diagnosis and to validate the morphological features of metastatic PDAC. METHODS AND RESULTS: Six IHC markers (CDX2, CK17, MEP1A, MUC2, PAX8, SMAD4) selected based on a literature review were stained on tissue microarrays containing 256 PDAC, 102 mucinous ovarian carcinomas (MC) and 58 mucinous borderline ovarian tumours (MBOT). Detailed morphological features were reviewed in 16 ovarian metastases from PDAC, 25 MC, and 9 MBOT. We confirmed that tumours with a size less than 13 cm, bilaterality, ovarian surface involvement, low-power nodularity, infiltrative invasion, pseudomyxoma ovarii despite cystadenoma or borderline areas, and moderate nuclear atypia should raise suspicion for metastatic PDAC and prompt evaluation with the recommended IHC panel. A 5-marker panel consisting of CK17, MEP1A, PAX8, SMAD4, and CDX2 had an overall accuracy of 91.8% (95% CI 88.8%-94.3%) using recursive partitioning, with the highest weight resting on CK17. CK17 was expressed in 80.9% of PDAC compared to 18.6% of MC and 1.7% of MBOT, respectively. CONCLUSIONS: This is the first ancillary IHC panel to distinguish between PDAC and OMT with high accuracy. These results inform further studies on diagnostic workflows tailored to the complexity of metastatic presentations of tumours at the ovary.
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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 | 0.003 | 0.001 |
| 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) | 0.002 | 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".