Establishment of patient-derived 3D in vitro models of sarcomas: literature review and guidelines on behalf of the FORTRESS working group
Bibliographic record
Abstract
Sarcomas are a large family of rare and heterogeneous mesenchymal tumors, which respond poorly to available systemic treatments. Translation of preclinical findings into clinical applications has been slow, limiting improvements in patients' outcomes and ultimately highlighting the need for a better understanding of sarcoma biology to develop more effective, subtype-specific therapies. To this end, reliable preclinical models are crucial, but the development of 3D in vitro sarcoma models has been lagging behind that of epithelial cancers. This is primarily due to the rarity and heterogeneity of sarcomas, and lack of widespread knowledge regarding the optimal growth conditions of these in vitro models. In this review, we provide an overview of currently available sarcoma tumoroid models, together with guidelines and suggestions for model development and characterization, on behalf of the FORTRESS (Forum For Translational Research in Sarcomas) international research working group on 3D sarcoma models.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".