Active Immune Phenotype in Head and Neck Cancer: Reevaluating the Iso-Effect Fractionation Based on the Linear Quadratic (LQ) Model—A Narrative Review
Bibliographic record
Abstract
Altered fractionation concepts and especially moderate hypo-fractionation are evaluated as alternatives to standard treatment for head and neck squamous cell carcinoma (HNSCC), associated with or not concurrent with or sequential to chemotherapy. The calculation of the iso-equivalent dose regimens has as its starting point the linear quadratic (LQ) formalism traditionally based on the “4Rs” of radiobiology. The higher rates of therapeutic failure after radiotherapy of HNSCC are associated with the heterogeneity of radio-sensibility. The identification of genetic signatures and radio-resistance scores aims to improve the therapeutic ratio of radiotherapy and to conceptualize personalized fractionation schemes. The new data regarding the involvement of the sixth “R” of radiobiology in HNSCC, especially for the HPV-driven subtype, but also for the “immune active” minority of HPV-negative HNSCCs, bring to the fore a multifactorial variation of the α/β ratio. The involvement of the antitumor immune response and the dose/fractionation/volume factors as well as the therapeutic sequence in the case of new multimodal treatments including immune checkpoint inhibitors (ICIs) could be included as an additional term in the quadratic linear formalism especially for hypo-fractionation regimens. This term should take into account the dual immunomodulatory effect (immunosuppressant and stimulator of antitumor immunity) of radiotherapy, which varies from case to case and can bring benefit or a detrimental effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".