Ovarian Mesonephric-like Adenocarcinoma: Its Prevalence in a Japanese High-Volume Cancer Center and a Literature Review on Therapeutic Targets
Bibliographic record
Abstract
Background: Ovarian mesonephric-like adenocarcinoma (MLA) is a newly described histological type known for its aggressive behavior. This study aims to determine the frequency of ovarian MLA, review the existing literature, and elucidate its clinicopathological characteristics, including the potential therapeutic targets. Methods: We retrospectively reviewed the pathological diagnoses of 501 primary ovarian cancer surgical cases at our institution from 2010 to 2023. MLAs exhibiting typical morphological and immunohistochemical features were included. The frequency and clinicopathological characteristics of these cases were summarized. Additionally, we conducted a literature search using PubMed to collect and summarize previously reported cases of ovarian MLAs. Results: Among the 501 primary ovarian cancer cases, we identified 3 cases (0.6%) of MLA. The patients were 52–76 years old, and the initial FIGO stages were IC1 (two cases) and IIIB (one case). All the cases exhibited HRP, pMMR, PD-L1 negativity (CPS < 1), and low HER2 expression. Two cases experienced metastatic recurrence. A literature review identified 97 cases of MLA. The MLAs frequently exhibited KRAS mutations (90%, 38/42), with a recurrence rate of 39% (26/67). Conclusion: MLAs accounted for 0.6% of malignant ovarian tumors at our institution, all of which were advanced or recurrent cases. These cases showed HRP, pMMR, and PD-L1 negativity, indicating a lack of current therapeutic targets. The literature also reported a high incidence of advanced and recurrent cases, highlighting the need for accurate diagnosis and the development of new treatments. The frequent KRAS mutations suggest a potential therapeutic target for recurrent or metastatic MLA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".