HNF-1β in Ovarian Carcinomas With Serous and Clear Cell Change
Bibliographic record
Abstract
Many ovarian tumors, including high-grade serous carcinoma (HGSC), show clear cell change. Accurate diagnosis is important, however, as ovarian clear cell carcinoma (OCCC) is known to be less responsive to traditional types of ovarian cancer chemotherapies. In a previous study, the clinical, morphologic, and immunohistochemical features of 32 ovarian carcinomas, which had been previously diagnosed as pure OCCC (n=11), pure HGSC (n=11), and mixed serous and clear cell (MSC) (n=10), were analyzed. The immunoreactivities of WT1, ER, and p53, as well as the mitotic indices and stages of presentation of the MSC, were similar to those of HGSC. It was consequently concluded that MSC represented HGSC with clear cell change. Hepatocyte nuclear factor-1β (HNF-1β) is a relatively new immunohistochemical marker that has been shown to be rather sensitive and specific for OCCC. We thus sought to evaluate this marker in this specific group of tumors. One block each of pure HGSC and pure OCCC were stained with HNF-1β. In the cases of MSC, 2 blocks were stained when the serous and clear cell components were not present on the same slide. None (0/11) of the pure HGSC showed immunoreactivity for HNF-1β, whereas all (11/11) of the pure OCCC were positive. In the cases of MSC, both the serous and clear cell components were negative for HNF-1β. HNF-1β seems to be a sensitive and specific marker for OCCC and is not expressed in HGSC with clear cell change. The pattern of immunoreactivity of HNF-1β in tumors with both serous and clear cell change supports the conclusion that MSC are HGSC with clear cells.
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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 | 0.001 |
| 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) | 0.000 | 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".