Superficial leiomyosarcoma: a clinicopathologic review and update
Bibliographic record
Abstract
BACKGROUND: Superficial leiomyosarcomas (SLMSs) are rare soft tissue malignancies. A clinicopathologic review of 25 cases was undertaken. METHODS: Twenty-five cases diagnosed between 1990 and 2007 were reviewed. Clinical information was obtained from patient charts. Histologic slides were reviewed, and immunohistochemical stains were performed. RESULTS: All patients presented with a nodule. Fourteen tumors were confined to the dermis and 11 involved subcutaneous tissue. Smooth muscle markers were positive in all cases. CD117 was consistently negative. Novel histological features included epidermal hyperplasia, sclerotic collagen bands and increasing tumor grade with the depth of the lesion. Poor outcome was associated with size > 2 cm, high grade and depth of the lesion. CONCLUSIONS: SLMSs are rare but important smooth muscle tumors of the skin. The clinical presentation may be non-specific. The histologic appearance is that of a smooth muscle lesion, but epidermal hyperplasia and thickened collagen bands are previously underrecognized features. Immunohistochemical stains are useful in confirming smooth muscle differentiation, but CD117 is of limited utility. SLMS can appear low grade or even benign on superficial biopsies, leading to undergrading or a delay in the correct diagnosis. Clinicians and pathologists alike should therefore be aware of these pitfalls and must approach these cases with caution.
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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 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".