Sinonasal Sarcomas Management: An International Consensus Statement
Bibliographic record
Abstract
INTRODUCTION: Sinonasal sarcomas are exceedingly rare entities, constituting less than 7% of head and neck sarcomas. Their complex histology needs specialized treatment, which is often based on multimodal approaches including surgery, radiation therapy, and/or chemotherapy. This manuscript aims to gather expert opinions to establish common management principles for sinonasal sarcomas. METHODS: This international consensus followed a modified Delphi method in seven steps, including statements definition by the core group, expert panel recruitment, and a two-round survey. Sixty-two statements on sinonasal sarcoma management were developed. Experts from multiple continents participated, and results were anonymized and analyzed between March and May 2025. RESULTS: A total of 44 invited experts were recruited, 43.2% otorhinolaryngologists/head and neck surgeons, 31.8% medical oncologists, and 25% radiation oncologists. Participants varied in age and experience, representing Europe (70.5%), North America (18.2%), South America (6.8%), and Asia (4.5%). Among all histologies, biphenotypic sarcoma, chondrosarcoma, leiomyosarcoma, and myofibrosarcoma are principally treated with an upfront surgical management, differently from Ewing sarcoma and rhabdomyosarcoma in which chemotherapy, eventually associated with radiotherapy, is often chosen. In the remaining histologies (angiosarcoma, liposarcoma, malignant peripheral nerve sheath tumor [MPNST], osteosarcoma, and synovial sarcoma), a precise multimodal treatment is less standardized and needs to be discussed on a case-by-case basis. CONCLUSION: Sinonasal sarcomas require a histology-driven approach to determine upfront treatment, whether surgical, medical, or multimodal. Despite this structured strategy, prognosis remains highly variable across subtypes. Multidisciplinary evaluation and individualized management in referral centers are crucial to address the biological diversity and anatomical complexity of these rare malignancies.
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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 | 0.099 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".