Malignant Epithelioid Hemangioendothelioma with Spindle Phenotype
Bibliographic record
Abstract
BACKGROUND: Epithelioid hemangioendothelioma (EHE) is a rare sarcoma of vascular differentiation that involves various sites and is rarely diagnosed by cytology. CASE: A thigh mass in a 36-year-old woman was aspirated. Cytologic examination showed a population of single cells of predominantly spindle morphology. The cells were large, with abundant cytoplasm, and possessed cytoplasmic processes. The nuclei were small and monomorphic and had a single conspicuous nucleolus. A minority of cells had a polygonal shape. Occasionally a perinuclear dense, round cytoplasmic condensation was present in the absence of a clear-cut intracytoplasmic luminae. Necrosis was noted. The tumor expressed vascular markers and was labeled as malignant EHE because of the spindle cell morphology, marked cellularity and presence of necrosis. CONCLUSION: On cytology, malignant EHE is a very difficult diagnosis to render, especially when the tumor displays spindle cell morphology. In such cases, the differential diagnostic list is that of spindle cell lesions, especially of fibroblastic-myofibroblastic nature. Including endothelial markers in the immunohistochemical panel is crucial to reach the diagnosis. Also, the presence of perinuclear cytoplarmic condensation should be interpreted as evidence of endothelial differentiation on cytology, even in the absence of clearcut intracytoplasmic luminae.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".