Head and neck cancer patient-derived tumouroid cultures: opportunities and challenges
Bibliographic record
Abstract
Head and neck cancers (HNC) are the seventh most prevalent cancer type globally. Despite their common categorisation, HNCs are a heterogeneous group of malignancies arising in various anatomical sites within the head and neck region. These cancers exhibit different clinical and biological manifestations, and this heterogeneity also contributes to the high rates of treatment failure and mortality. To evaluate patients who will respond to a particular treatment, there is a need to develop in vitro model systems that replicate in vivo tumour status. Among the methods developed, patient-derived cancer organoids, also known as tumouroids, recapitulate in vivo tumour characteristics including tumour architecture. Tumouroids have been used for general disease modelling and genetic instability studies in pan-cancer research. However, a limited number of studies have thus far been conducted using tumouroid-based drug screening. Studies have concluded that tumouroids can play an essential role in bringing precision medicine for highly heterogenous cancer types such as HNC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".