Malignant Sinonasal Tumors: Update on Histological and Clinical Management
Bibliographic record
Abstract
Tumors of nasal cavity and paranasal sinuses (TuNSs) are rare and heterogeneous malignancies, presenting different histological features and clinical behavior. We reviewed the literature about etiology, biology, and clinical features of TuNSs to define pathologic features and possible treatment strategies. From a diagnostic point of view, it is mandatory to have high expertise and perform an immunohistochemical assessment to distinguish between different histotypes. Due to the extreme rarity of these neoplasms, there are no standard and evidence-based therapeutic strategies, lacking prospective and large clinical trials. In fact, most studies are retrospective analyses. Surgery represents the mainstay of treatment of TuNSs for small and localized tumors allowing complete tumor removal. Locally advanced lesions require more demolitive surgery that should be always followed by adjuvant radio- or chemo-radiotherapy. Recurrent/metastatic disease requires palliative chemo- and/or radiotherapy. Many studies emphasize the role of specific genes mutations in the development of TuNSs like mutations in the exons 4-9 of the TP53 gene, in the exon 9 of the PIK3CA gene and in the promoter of the TERT gene. In the near future, this genetic assessment will have new therapeutic implications. Future improvements in the understanding of the etiology, biology, and clinical features of TuNSs are warranted to improve their management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".