Myxoid Mesenchymal Tumors of the Uterus: An Update on Classification, Definitions, and Differential Diagnosis
Bibliographic record
Abstract
Tumors with a predominant myxoid stroma are rare in the uterus. When encountered, however, they pose a diagnostic challenge. Traditionally myxoid leiomyosarcoma has been the most important consideration in this category, given its adverse prognosis and deceptively bland morphology. Conventional features of malignancy are variably present; in contrast, an infiltrative tumor border is a consistent pathologic characteristic. More recently, previously under-recognized lesions have been identified, in part due to our growing knowledge of their underlying molecular alterations: uterine inflammatory myofibroblastic tumor frequently harbors ALK rearrangements and a novel ZC3H7B-BCOR gene fusion has been described in a subset of myxoid high-grade endometrial stromal sarcomas. These tumors need to be distinguished from myxoid leiomyosarcoma, as by comparison have a less aggressive course and are amenable to targeted treatments. In addition, uterine mesenchymal tumors with malignant potential need to be distinguished from benign tumors and epithelial and mixed malignancies. This review aims to discuss our current understanding of the most common uterine myxoid neoplasms: their clinical features, their distinguishing histopathologic, immunohistochemical, and molecular features and the clues and pitfalls in their diagnosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".