Mesonephric-like Adenocarcinoma (MLA) Diagnostic Criteria and Controversies: Perspectives and Guidance From Pathologists in the MLA Consortium
Bibliographic record
Abstract
Mesonephric-like adenocarcinoma (MLA) is a rare and aggressive gynecologic malignancy that has only been recognized in the last decade. It arises in the endometrium, ovaries, and other extrauterine sites (often in association with endometriosis) and closely mimics a variety of other tumor types that occur in these locations. While it shows significant morphologic, immunohistochemical, and molecular homology with cervical mesonephric adenocarcinoma, there are many clinicopathologic features that suggest müllerian derivation, and this is now well established. As research on MLA has accumulated, questions have emerged about optimal practices for the diagnosis of these challenging tumors. In 2022, faculty at M.D. Anderson Cancer Center convened the Mesonephric-like Adenocarcinoma (MLA) Consortium, comprised of international pathologists, gynecologic oncologists, medical oncologists, radiation oncologists, and basic science investigators with expertise in MLA, with the goals to enhance understanding of these tumors, refine diagnostic criteria, improve treatment options, and facilitate research collaborations. An initial review from the consortium was published in 2025, and included diagnostic recommendations from the group's pathologists. Controversies remain, however, about the morphologic, immunohistochemical, and molecular criteria that should be used to establish a diagnosis of MLA. Herein, the pathologists from the MLA Consortium provide a comprehensive evaluation of the literature on MLA diagnostic criteria, address ongoing controversies in this area, and provide practical guidance for pathologists considering this entity.
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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 | 0.075 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".