Chemotherapy-free innovations in locally advanced head and neck cancer: a comprehensive review
Bibliographic record
Abstract
The treatment of locally advanced head and neck squamous cell carcinoma (LA-HNSCC) has traditionally relied on a multimodal approach, combining surgery, radiation therapy (RT), and chemotherapy. While chemotherapy plays a critical role in improving cure rates and functional outcomes, its substantial toxicity remains a major concern, particularly in older patients. These challenges are especially relevant for those who are unfit for chemotherapy or decline conventional concurrent chemoradiotherapy (CCRT), highlighting the need for alternative therapeutic options. Many patients are at high risk for severe side effects, often preventing them from completing the full chemotherapy regimen. This review explores alternative strategies to definitive CCRT of carcinomas of the larynx, hypopharynx and oropharynx, aiming to optimize treatment outcomes while minimizing toxicity. We discuss altered fractionation strategies as a promising alternative to conventional RT, offering a balance between treatment efficacy and quality of life. Additionally, we examine emerging approaches, including the combining of targeted therapies, immunotherapy, hyperthermia, photodynamic therapy and nanoparticle-based treatments with RT, which provide alternative or complementary options to traditional therapies in the management of LA-HNSCC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".