Cytohistologic correlations in 56 synovial sarcomas in 36 patients: The Institut Curie experience
Bibliographic record
Abstract
Synovial sarcoma (SS) is a high-grade malignant soft tissue tumor that manifests different phenotypic subtypes that may render their cytologic evaluation challenging. Although several cytologic studies of SS have been published, correlative studies of cytologic and corresponding histologic features are limited. To better define the cytological features of various SS forms, we reviewed the cytologic and the corresponding histologic material of 56 tumors from 36 patients. Classical patterns were defined as dispersed or small clusters of cells with bland chromatin, inconspicuous nucleoli, oval to spindle-shaped cytoplasm and branching tumor tissue fragments, vessel stalks, acinar structures in scant mucin background, seen in all 53 (94.7%) cellular cases. Epithelial, squamous, round cells, mast cells, necrosis, comma-like nuclei, marked nuclear atypia, secretory mucin, and rosette-like structures were also occasionally observed. Comparing the histological subtype we noted that epithelial cells and secretory mucin were restricted to biphasic SS, round cells to poorly differentiated SS, and comma-like nuclei to monophasic fibrous SS. We conclude that the classical pattern is highly suggestive of SS of all three monophasic, biphasic, or poorly differentiated subtypes. These characteristics, along with molecular genetic studies, may improve the cytologic diagnosis of SS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".