Pleomorphic Undifferentiated Uterine Sarcoma in a Young Patient Presenting With Elevated Beta-hCG and Rare Variants of Benign Leiomyoma: A Case Report and Review of the Literature
Bibliographic record
Abstract
Leiomyosarcoma (LMS) is a malignant mesenchymal neoplasm showing smooth muscle differentiation. Uterine LMS is more frequent that nonuterine LMS, and represents 1% of all malignant neoplasms of the uterus. Pleomorphic undifferentiated uterine sarcoma is a rare entity, and is defined by high-grade sarcoma histology with loss of muscular markers. Several cases of pleomorphic undifferentiated uterine sarcoma have been reported in the literature, with worse clinical outcome when compared with conventional LMS. Here we report the first case of a pleomorphic undifferentiated uterine sarcoma in association with LMS in a 33 yr old woman. The patient presented clinically with recurrent vaginal bleeding and suspicion of a trophoblastic tumor. Ancillary testing revealed moderately elevated beta-hCG (49.7 U/L) and no metastatic disease on imaging. Gross examination of the hysterectomy specimen revealed a large heterogenous necrotic uterine mass infiltrating <50% of the myometrium. Microscopic evaluation showed pleomorphic undifferentiated uterine sarcoma adjacent to a nodule of leiomyoma with bizarre nuclei, with loss of myogenic markers in the high grade component. Other findings included a foci of conventional LMS, and diffuse uterine leiomyomatosis. Although beta-hCG dropped to normal levels during follow-up, the patient developed metastatic lesions to the lung at 6 mo postop. Initial elevation of beta-hCG may have correlated with the aggressive histology of the tumor, as reported by some groups previously. Recognition of pleomorphic undifferentiated uterine sarcoma and its distinction from conventional LMS is essential for patient prognosis and management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".