What is the future of treatment de-escalation for HPV-positive oropharyngeal cancer? A review of ongoing clinical trials
Bibliographic record
Abstract
Background: Human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) has increased in incidence in recent decades. With higher cure rates in younger populations, long-term survivors may live with acute- and long-term toxicity, leading to increased interest in de-escalation treatment strategies for HPV-related OPSCC. Herein, we have examined the current landscape of clinical trials in this context. Methods: A review of active clinical trials related to de-escalation of HPV-associated OPSCC treatment was performed using the clinicaltrials.gov database from inception to January 2022. A search using the key words "oropharyngeal cancer" and "HPV" was completed. Three investigators independently reviewed each trial, with any discrepancies settled by a fourth. Data collected from each study included study phase, study design, primary, and secondary endpoints, and de-escalation treatment strategies. A final 24 articles were selected for full text review. Results: Many trials (n=19, 79%) were non-randomized, and most studies employed a phase II design (n=14, 58%). Only 13% (n=3) were randomized trials, and 8% (n=2) included a phase III component. The most frequent primary endpoint was progression-free survival (PFS) (n=9, 37.5%). With regards to the identified de-escalation strategies, all the studies (n=24) had at least one component assessing changes in RT dose/fractionation and/or a reduction in RT volumes. A smaller percentage of trials assessed surgical interventions (n=9, 37.5%) and/or changes in systemic therapy (n=8, 33.3%). Conclusion: A small number of randomized trials are underway, and a transition to more randomized phase III trials in the future will be important to change clinical practice.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".