Uterine Smooth Muscle Tumor of Uncertain Malignant Potential: Clinicopathologic-Sonographic Characteristics, Follow-Up and Recurrence
Bibliographic record
Abstract
BACKGROUND: Uterine smooth muscle tumor of uncertain malignant potential (STUMP) is rare tumor, and regarded as sub-classification in uterine smooth muscle tumors between benign and malignant criteria. In this study, we evaluated characteristics of cases with STUMP diagnosis in a 10-year period. METHODS: We retrospectively evaluated medical records of patients with histopathological STUMP diagnosis in Istanbul Training and Research Hospital, a tertiary center. We analyzed preoperative demographic, clinical features and postoperative follow-up. Preoperative sonographic data were re-evaluated. RESULTS: The mean age was 42 years. One patient was postmenopausal, and five patients were premenopausal. All of them had a complaint of meno-metrorrhagia. We re-evaluated preoperative sonographic images of patients, and defined 83.3% as well-defined margins, 66.7% hyperechoic, 100% heterogeneous, 66.7% non-cystic, 50% calcification and 66.7% acoustic shadowing. Pathologic features showed mean number of mitosis 8, mild atypia 66.7%, and necrosis 33.3%. In a 24-year-old unmarried female patient with myomectomy, we detected recurrance of tumor in sonographic and MRI studies after 11 months, and confirmed the diagnosis via tru-cut biopsy. There was no relevance between sonographic findings and atypia, necrosis and mitosis. The recurrence was not in relationship with mitosis, degree of atypia and necrosis. We found no relevance between tumor diameter and mitosis, atypia, necrosis and recurrence. CONCLUSIONS: STUMP is classified as an intermediate form, histopathologically so calling it benign or malignant for sure is not possible. Singulary, solidity, hyperechogenicity, heterogenecity and features of acoustic shadowing and margins can guide us to preoperative sonographic diagnosis. Recurrence/metastasis after many years from operation can be seen, and those patients should be followed long term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".