Pre-Clinical In Vitro Models Used in Cancer Research: Results of a Worldwide Survey
Bibliographic record
Abstract
To develop and subsequently get cancer researchers to use organotypic three-dimensional (3D) models that can recapitulate the complexity of human in vivo tumors in an in vitro setting, it is important to establish what in vitro model(s) researchers are currently using and the reasons why. Thus, we developed a survey on this topic, obtained ethics approval, and circulated it throughout the world. The survey was completed by 101 researchers, across all career stages, in academia, clinical or industry settings. It included 40 questions, many with multiple options. Respondents reported on their field of cancer research; type of cancers studied; use of two-dimensional (2D)/monolayer, 2.5D and/or 3D cultures; if using co-cultures, the cell types(s) they co-culture; if using 3D cultures, whether these involve culturing the cells in a particular way to generate spheroids, or if they use additional supports/scaffolds; techniques used to analyze the 2D/2.5D/3D; and their downstream applications. Most researchers (>66%) only use 2D cultures, mainly due to lack of experience and costs. Despite most cancer researchers currently not using the 3D format, >80% recognize their importance and would like to progress to using 3D models. This suggests an urgent need to standardize reliable, robust, reproducible methods for establishing cost-effective 3D cell culture models and their subsequent characterization.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".