Tegafur–Uracil Maintenance Therapy in Non-Metastatic Head and Neck Cancer: An Exploratory Systematic Review
Bibliographic record
Abstract
BACKGROUND: Tegafur-uracil (UFT), an oral fluoropyrimidine developed in Asia, has been investigated as a maintenance or adjuvant therapy in various malignancies. Its use in head and neck cancers, however, remains limited to small retrospective studies, primarily from East Asia. Given the need for cost-effective maintenance strategies in resource-limited settings, we conducted an exploratory systematic review to evaluate the clinical utility of UFT in non-metastatic head and neck squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC). METHODS: We systematically searched PubMed, EMBASE, and Cochrane Library from inception through 1 May 2025 for retrospective cohort studies evaluating UFT after definitive therapy in non-metastatic HNSCC or NPC. Study selection followed PRISMA guidelines. Given the heterogeneity of included studies, we performed a structured narrative synthesis using the SWiM (Synthesis Without Meta-analysis) framework to summarize survival outcomes, treatment settings, and clinical contexts. RESULTS: Seven retrospective studies (four HNSCC, three NPC) involving 508 patients were included. UFT was generally administered at 300-400 mg/day for 6-12 months. Across studies, UFT use was associated with favorable disease-free and overall survival trends in high-risk subgroups, including patients with extranodal extension and persistent EBV DNA. Treatment adherence and toxicity profiles were acceptable. CONCLUSIONS: While the evidence remains limited and heterogeneous, this review highlights recurring signals of benefit associated with UFT maintenance therapy in selected high-risk patients. Prospective trials are warranted to confirm these findings and better define a possible role of UFT in maintenance therapy in some advanced non-metastatic HNSCC and 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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".