Liquid Biopsies in Head and Neck Cancers: Recent Developments Across Biofluids, Analytes, and Molecular Features
Bibliographic record
Abstract
BACKGROUND: Head and neck cancers (HNCs) are too often diagnosed at advanced stages when outcomes are poor. Additionally, robust tools for the early detection of recurrence remain elusive. These gaps drive interest in so-called liquid biopsy approaches for HNC detection, prognostication, and surveillance. Molecular heterogeneity presents challenges to liquid biopsy testing, but emerging approaches provide promising avenues toward clinical utility. METHODS: We review the latest developments in HNC liquid biopsies, provide perspectives on viral-associated and nonviral-associated cancers, and assess various biofluids, analytes, and molecular profiling approaches. RESULTS: Liquid biopsy assays targeting viral DNA from peripheral blood plasma have established clinical performance, and utility studies are ongoing, serving as a blueprint for other emerging assays. CONCLUSION: The use of multiple biofluid sources and analytes may improve detection sensitivity and clinical applicability. Standardization and harmonization of analysis methods will be critical for enhancing biomarker discovery and enabling reliable clinical implementation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".