Prognostic Factors in Sinonasal Cancers: A Multicenter Pooled Analysis
Bibliographic record
Abstract
OBJECTIVES: Sinonasal cancers (SNC) are heterogeneous diseases with different clinical behavior. We aimed to identify prognostic factors in non-metastatic (M0)-SNC. METHODS: Electronic health records from M0-SNC patients treated with definitive surgery ± postoperative radiotherapy or chemoradiotherapy at two tertiary institutions were reviewed. p16 staining was performed in the squamous cell carcinoma (SCC) subset. Multivariable analysis (MVA) calculated the adjusted hazard ratio (aHR) for histology type (SCC as the comparator), T/N categories, and primary treatment modality for the risk of locoregional failure (LRF), distant metastasis (DM), and deaths. RESULTS: A total of 376 patients were eligible including 209 (56%) SCC (p16+: 35; p16-/untested: 157), 42 (11%) adenocarcinoma, 35 (9%) sinonasal undifferentiated carcinoma or sinonasal neuroendocrine tumors (SNUC/SNEC), 33 (9%) mucosal melanoma (MM), 30 (8%) esthesioneuroblastoma (ES), and 27 (7%) adenoid cystic carcinoma (ACC). MVA identified MM histology (aHR 2.03, 95% CI 21.23-3.33), older age (aHR 1.02; 95% CI: 1.00-1.03), T3-4 tumor (aHR 5.08, 95% CI 2.77-9.30), and nodal involvement (aHR: 2.15, 95% CI 1.46-3.16) carried higher mortality risk (all p < 0.05); MM (aHR 10.14, 95% CI 4.90-21.01), ACC (aHR 2.97, 95% CI 1.27-6.96), and SNUC/SNEC (aHR 6.80, 95% CI 3.30-14.01) histologies and T3-4 categories (vs. T1-2, HR 4.79, 95% CI 1.53-14.95) had higher DM risk; T3-4 (aHR 2.33, 95% CI 1.37-3.97) and nodal involvement (aHR 1.70, 95% CI 1.11-2.60) conveyed higher LRF risk while SNUC/SNEC histologies had a lower LRF risk (aHR 0.51, 95% CI 0.26-3.33). CONCLUSIONS: Different SNC histology types exhibit distinct patterns of relapse and survival, highlighting the need for different management strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".