Fine‐needle aspiration of renal and extrarenal rhabdoid tumors
Bibliographic record
Abstract
BACKGROUND: Rhabdoid tumors (RT) are rare, renal or extrarenal, high-grade malignancies. The cytologic diagnosis may be confirmed if combined with genomic results. In the current study, the authors present the cytologic and ancillary techniques used to diagnose RT in their series of 20 tumors in 13 patients. METHODS: Clinical charts as well as cytologic, histologic, karyotypic, and molecular biology results were reviewed. RESULTS: Twelve fine-needle aspirations (FNAs) were performed for primary diagnosis, 7 were to confirm a metastasis, and 1 was to confirm local recurrence. Primary tumors were in the kidney in 7 cases and 13 were extrarenal. Patient age ranged from 5 months to 26 years. There were 7 females and 6 males. FNAs were cell-rich in 16 cases and cell-poor in 4 cases and revealed a mix of atypical spindle-shaped, round, rhabdoid, or epithelioid cells, singly or in clusters. Mitosis and necrosis occasionally were present. The original cytologic diagnosis was malignant in all cases. There were no unsatisfactory or false-negative samples. In the 12 primary tumors, the preliminary FNA diagnosis was RT in 7 cases (58%), rhabdomyosarcoma in 4 cases (33%), and malignant peripheral nerve sheath tumor in 1 case (8%). Karyotypes were available in 6 cases, 3 of which demonstrated chromosome 22 changes. Fluorescence in situ hybridization revealed loss of probe signals for the SMARCB1 gene locus in 5 cases; DNA sequence analysis performed in 9 cases revealed deletions in codons of the SMARCB1 gene in 7 cases and a mutation in 2 cases. CONCLUSIONS: The primary diagnosis of RT is possible on FNA. In the current study, 12 of 13 cases were diagnosed by FNA with a combination of clinical information, immunocytochemistry, and molecular analysis.
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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 | 0.002 |
| 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) | 0.001 | 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".