Cytohistologic correlations in angiosarcoma including classic and epithelioid variants: Institut Curie's experience
Bibliographic record
Abstract
To characterize the cytological features of angiosarcomas, we reviewed the fine-needle aspiration material and corresponding histologic sections of 29 tumors in 23 patients. Histologically, 24 tumors were of the classic type, and 5 were epithelioid angiosarcomas. The original corresponding cytologic diagnoses were as follows: angiosarcoma, 17 cases; sarcoma not otherwise specified, 8 cases; and rhabdomyosarcoma, 1 case. Three samples were cell-poor and were considered suspicious of malignancy. The review of cytology samples showed that smears were cell-rich in 17 tumors and cell-poor in 12 tumors. A hemorrhagic background was present in 9 cases. Tumor cells were polymorphous, including spindle-shaped, round to oval, and polygonal epithelioid cells and giant cells in different proportions. Erythrophagocytosis was seen in 12 tumors. Smears of classic angiosarcomas were polymorphous and lacking specific characteristics, whereas smears of epithelioid tumors were morphologically similar and composed of round to oval and polygonal, epithelial cells frequently arranged in clusters, and showing erythrophagocytosis. The wide spectrum of cellular components of angiosarcomas accounts for the difficulty in establishing accurate tumor typing, particularly with cell-poor samples and low-grade classic angiosarcoma. Entities to consider in the differential diagnosis are carcinoma, epithelioid sarcoma, pleomorphic rhabdomyosarcoma, and malignant melanoma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".