In vitro models for head and neck cancer: Current status and future perspective
Bibliographic record
Abstract
The 5-year overall survival rate remains approximately 50% for head and neck (H&N) cancer patients, even though new cancer drugs have been approved for clinical use since 2016. Cancer drug studies are now moving toward the use of three-dimensional culture models for better emulating the unique tumor microenvironment (TME) and better predicting in vivo response to cancer treatments. Distinctive TME features, such as tumor geometry, heterogenous cellularity, and hypoxic cues, notably affect tissue aggressiveness and drug resistance. However, these features have not been fully incorporated into in vitro H&N cancer models. This review paper aims to provide a scholarly assessment of the designs, contributions, and limitations of in vitro models in H&N cancer drug research. We first review the TME features of H&N cancer that are most relevant to in vitro drug evaluation. We then evaluate a selection of advanced culture models, namely, spheroids, organotypic models, and microfluidic chips, in their applications for H&N cancer drug research. Lastly, we propose future opportunities of in vitro H&N cancer research in the prospects of high-throughput drug screening and patient-specific drug evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".