Navigating the challenges of immunotherapy as a neoadjuvant treatment for locally advanced squamous cell carcinoma of the head and neck: A review of the evidence from the literature
Bibliographic record
Abstract
Locally advanced squamous cell carcinoma of the head and neck (LA-HNSCC) is the most common stage at diagnosis for this disease. Despite significant advancements in cure rates through surgery and chemoradiotherapy, recurrence rates remain high, underscoring the need for alternative strategies to improve outcomes. Immunotherapy, particularly PD-1 inhibitors, has become the standard of care in recurrent or metastatic settings, sparking interest in their use in LA-HNSCC. This review synthesizes the findings of clinical trials investigating neoadjuvant immunotherapy over the past five years, highlighting its potential to improve overall survival and prevent recurrence. We emphasize the substantial heterogeneity of these studies in terms of treatment regimens, combinations, and durations, which complicates the interpretation and comparison of results. Additionally, we discuss the potential role of immunotherapy in tailoring subsequent treatment strategies based on response. We also address key challenges in clinical trial design, such as the selection of appropriate endpoints (e.g., pathological response, toxicity, and time-to-event endpoints like disease-free survival or overall survival) and the integration of biomarkers to predict outcomes. Preliminary findings suggest that adding chemotherapy or other checkpoint inhibitors to PD-1 inhibitors may enhance pathological responses, although further research is needed to determine if this translates into longer survival and improved quality of life. We emphasize the need to identify predictive biomarkers for patient selection and establish consensus in trial design to facilitate result comparisons. In conclusion, while neoadjuvant immunotherapy offers promising opportunities, ongoing research is crucial to refine its clinical application and maximize its therapeutic potential in LA-HNSCC patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".