Biphenotypic Sinonasal Sarcoma: Case Reports of Diagnostic Findings and Clinical Management of 3 Cases
Bibliographic record
Abstract
BACKGROUND: Biphenotypic sinonasal sarcoma (BSNS) is a rare spindle cell sarcoma distinctly arising in the sinonasal area, with dual myogenic and neural differentiation, and characterised by the presence of PAX3 gene fusion, typically with MAML3. Although the majority may be indolent, up to 25% of cases reported in the literature are locally aggressive, with invasion of adjacent critical structures in the head and neck region. CASE REPORT: We report 3 cases of BSNS reviewed at our institution between 2016-2020 in addition to the current literature. Patient 1 underwent surgery followed by adjuvant radiotherapy but relapsed 24 months later and was not fit for systemic anticancer therapy and managed with palliative care. Due to comorbidities, patient 2 was recommended for active surveillance, with a view to intervening with radiotherapy should there be evidence of clinical progression. At 60 months, the nasal cavity mass remained stable on serial imaging. Patient 3 underwent primary surgical R0 resection and was offered adjuvant post operative radiotherapy 60 Gy/30 fractions/6 weeks but opted for active surveillance and has no clinical or radiological evidence of recurrence 22 months after surgery. CONCLUSION: The primary management for BSNS is surgical resection. We recommend discussing the role of postoperative adjuvant radiotherapy 60 Gy/30 fractions/6 weeks in patients who are fit for treatment. In clinical practice, dose levels will be constrained by surrounding normal tissues. At present, the role of systemic anticancer therapy is undefined. A prospective registry of ultra-rare cases may provide an evidence base with which to select optimal treatment strategies for BSNS in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".