Cytohistologic correlations in schwannomas (neurilemmomas), including “ancient,” cellular, and epithelioid variants
Bibliographic record
Abstract
Schwannoma accounts for one of the most common benign mesenchymal neoplasms of soft tissues. Although it is well defined in the cytology literature, particular histologic subtypes such as "ancient," cellular and epithelioid variants could be a source of diagnostic difficulties. We have reviewed cytology aspirates and corresponding histologic sections from 34 schwannomas diagnosed at Institut Curie. Histologically, 24 cases were classic, 5 were "ancient," 4 were cellular, and 1 was epithelioid schwannomas. No example of melanotic schwannoma was recorded. Original cytologic diagnosis was schwannoma in 13 (38.2%) cases, benign soft tissue tumor in 11 (32.4%), pleomorphic adenoma in 2 (6%) cases, angioma in 1 (2.9%) case, nodular fasciitis in 1 (2.9%) case, suspicious in 3 (8.8%) cases, and not satisfactory in 3 (8.8%) cases. There were no major differences between classical, "ancient," cellular, and epithelioid variants on cytology smears. Myxoid stroma, mast cells, and intranuclear inclusions were limited to classical subtype. Similarly, cyto-nuclear atypia was more frequent in classical subtype than in other subtypes. Schwannoma should be differentiated from well-differentiated malignant peripheral nerve sheath tumor, neurofibroma, and pleomorphic adenoma, in the last instance particularly for head and neck lesions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".