Microscopic Extranodal Extension in HPV‐Negative Head and Neck Cancer and the Role of Adjuvant Chemoradiation
Bibliographic record
Abstract
Objective Pathologic extranodal extension (ENE) is an important adverse feature for human papillomavirus (HPV)–negative head and neck squamous cell carcinoma (HNSCC), but the prognostic significance of microscopic ENE (ENEmi) and role of adjuvant concurrent chemoradiation (CRT) for ENEmi remain unclear. This study evaluates (1) the prognostic significance of ENEmi in HPV‐negative HNSCC and (2) whether adjuvant CRT is associated with improved overall survival (OS) for these patients. Study Design Retrospective cohort study. Setting Commission on Cancer (CoC)–accredited facilities. Methods This retrospective cohort study included patients in the National Cancer Database from 2009 to 2015 with pathologic node‐positive (pN+) HPV‐negative HNSCC with either pathologic ENEmi or no ENE who had undergone margin‐negative surgery. The association of ENEmi with OS was evaluated using Cox proportional hazard analyses. Analyses were repeated in patients with ENEmi receiving adjuvant therapy to evaluate the association of adjuvant CRT with OS. Results We included 5483 patients with pN+ HPV‐negative HNSCC, of whom 24% had ENEmi. On multivariable analysis, ENEmi was associated with decreased OS relative to no ENE (adjusted hazard ratio [aHR], 1.43; 95% CI, 1.28‐1.59). Among patients with ENEmi who received ≥60 Gy of adjuvant radiation therapy (RT) (n = 617), adjuvant CRT was not associated with improved OS relative to RT (aHR, 0.91; 95% CI, 0.66‐1.27). Conclusion For patients with HPV‐negative HNSCC, pN+ with ENEmi is associated with worse OS than pN+ without ENE. However, for patients with ENEmi, concurrent CRT is not associated with improved OS relative to RT. The optimal adjuvant paradigm for ENEmi requires additional investigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".