Updates on Treatments and Management of Nasopharyngeal Carcinoma
Bibliographic record
Abstract
Nasopharyngeal carcinoma (NPC) is a unique head and neck cancer, where the endemic subtype is strongly associated with Epstein-Barr virus (EBV) infection, whereas emerging data suggest that a subset of nonendemic NPC may be associated with human papillomavirus (HPV) infection. Nonetheless, treatment advances have been driven by clinical trials conducted in endemic NPC, investigating optimal sequencing of chemotherapy and immune checkpoint inhibitors with radiotherapy for locoregionally advanced disease. The preference for induction chemotherapy (IC) in these patients has also led to evolution in the concept of radiotherapy target delineation. Because of its association with EBV, plasma EBV DNA is an archetypal biomarker for endemic NPC, and it is being explored for precise stratification and treatment individualization in several ongoing trials. In the space of recurrent or metastatic-NPC, with the advent of platinum-doublet chemotherapy and anti-PD-1 antibody as the new standard of care, several trials are investigating new immunotherapeutic combinations, bispecific antibodies, and antibody-drug conjugates that have demonstrated promise in early phase trials. An important advance for NPC in 2025 is the update of the 9th version of the TNM staging system, which has introduced several key changes, including downgrading of the TNM stage groupings for localized disease, and splitting of metastatic NPC into IVA and IVB based on the number of metastatic lesions. These revisions would have implications for the treatment and design of future trials. These advances are also relevant to nonendemic NPC, where evidence is inconclusive whether this disease responds differently to current treatments compared with endemic NPC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".