Sinonasal Squamous Cell Carcinoma, a Narrative Reappraisal of the Current Evidence
Bibliographic record
Abstract
Sinonasal squamous cell carcinoma is a rare tumor affecting the nasal cavity and paranasal sinuses. Several aspects of this disease, ranging from epidemiology to biology, pathology, diagnosis, staging, treatment, and post-treatment surveillance are controversial, and consensus on how to manage this sinonasal cancer is lacking. A narrative literature review was performed to summarize the current evidence and provide the reader with available data supporting the decision-making process in patients affected by sinonasal squamous cell carcinoma, alongside the authors' personal opinion on the unsolved issues of this tumor. The review has highlighted several advances in molecular definition of epithelial cancers of the sinonasal tract. Surgery represents the pivot of treatment and is performed through an endoscopic transnasal approach whenever feasible. Open surgery is required for a large proportion of cases. Reconstruction of the defect follows principles of skull base and cranio-maxillo-facial reconstruction. Chemotherapy is given as neoadjuvant treatment or concomitantly to radiotherapy. Photon-based radiation therapy has a crucial role in the adjuvant setting. Particle therapy is providing promising results. Management of the neck should be planned based on the presence of clinically appreciable metastases, primary tumor extension, and need for recipient vessels. Biotherapy and immunotherapy are still underexplored therapeutical modalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".