Contemporary Opportunities in Nonsurgical Management of Locoregionally Advanced Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Abstract The majority of head and neck squamous cell carcinoma (HNSCC) is now classified into two major types: HPV-mediated [HPV(+)] and HPV-negative [HPV(−)]. Within this paradigm, the 8th edition TNM staging system effected modification about what is considered “locally-advanced” HNSCC. Two phase-III trials (RTOG 1016 and De-ESCALATE HPV) disappointingly showed thatcetuximabis not as effective in HPV(+) oropharyngeal cancer (OPC) compared tocisplatinwith radiotherapy. The recent NRG HN002 de-escalation trial demonstrated the presence of outcome heterogeneity within “low-risk” HPV(+) OPC, some of which continue to benefit fromcisplatincombined with reduced-dose radiotherapy. Moreover, distant metastasis (DM) has consolidated its position as the leading cause of death in HPV(+) OPC and strategies to mitigate it are necessary. Unanswered questions and ongoing-emerging concepts exist in both HPV(+) and HPV– diseases. These include understanding the importance of risk under the rubric of extranodal extension (ENE), including degrees of pathological ENE (pENE), and emerging knowledge about radiologic ENE (rENE). Strategies addressing modification of biological phenomena have become paramount and includes hypoxia modification (such as smoking cessation). In addition, contemporary evidence suggests that immunotherapy improves survival in recurrent/metastatic settings, and it is now also being explored in primary disease presentations in combination with (chemo-)radiotherapy. Induction chemotherapy achieves DM reduction in nasopharyngeal cancer but has only been explored minimally in HPV(+) OPC. Evidence that loco-regional management can be de-intensified following a favorable response to induction treatment would provide an attractive option for HPV(+) OPSCC patients while also addressing risk of developing distant disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".