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Record W4319930498 · doi:10.1038/s41416-023-02167-4

Head and neck cancer patient-derived tumouroid cultures: opportunities and challenges

2023· review· en· W4319930498 on OpenAlexfundno aff
B. W. M. Thilini J. Basnayake, Paul Leo, Sudha Rao, Sarju Vasani, Liz Kenny, Nikolas K. Haass

Bibliographic record

VenueBritish Journal of Cancer · 2023
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerNational Health and Medical Research CouncilServierNational Institutes of HealthCancer AustraliaGarnett Passe and Rodney Williams Memorial FoundationRoyal Brisbane and Women's Hospital FoundationMedical Research CouncilCancer Research Institute
KeywordsHead and neck cancerCancerHead and neckMedicineDiseasePrecision medicineIn vivoTumour heterogeneityOncologyBioinformaticsPathologyInternal medicineBiologySurgeryGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.173
GPT teacher head0.386
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations17
Published2023
Admission routes1
Has abstractyes

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