Potential Limitations of Bioluminescent Xenograft Mouse Models: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Bioluminescent imaging (BLI) is a versatile technique that offers non-invasive and real-time monitoring of tumor development in preclinical cancer research. However, the technique may be limited by several factors that can lead to misinterpretation of the data. This review aimed to investigate the validity of current BLI tumor models and provide recommendations for future model development. METHODS: Two major databases, MedLine and EMBASE, were searched from inception to July 2018 inclusively. Studies utilizing mouse xenograft models with demonstration of linear correlations between bioluminescent signal and tumor burden were included. Coefficients of correlation and determination were extracted along with data relating to animal model parameters. RESULTS: 116 studies were included for analysis. It was found that the majority of models demonstrate good correlation regardless of the model type. Selection of a single cell clone with highest luciferase expression resulted in a significantly better correlation. Lastly, appropriate tumor measurement techniques should be utilized when validating the BLI model. CONCLUSIONS: In general, BLI remains a valid tool for pre-clinical assessment of tumor burden. While no single factor may be identified as a general limitation, data should be interpreted with caution.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".