Multimodality cellular and molecular imaging of concomitant tumour enhancement in a syngeneic mouse model of breast cancer metastasis
Bibliographic record
Abstract
The mechanisms that influence metastatic growth rates are poorly understood. One mechanism of interest known as concomitant tumour resistance (CTR) can be defined as the inhibition of metastasis by existing tumour mass. Conversely, the presence of a primary tumour has also been shown to increase metastatic outgrowth, termed concomitant tumour enhancement (CTE). The majority of studies evaluating CTR/CTE in preclinical models have relied on endpoint histological evaluation of tumour burden. The goal of this research was to use conventional magnetic resonance imaging (MRI), cellular MRI, and bioluminescence imaging to study the impact of a primary tumour on the development of brain metastases in a syngeneic mouse model. Here, we report that the presence of a 4T1 primary tumour significantly enhances total brain tumour burden in Balb/C mice. Using in vivo BLI/MRI we could determine this was not related to differences in initial arrest or clearance of viable cells in the brain, which suggests that the presence of a primary tumour can increase the proliferative growth of brain metastases in this model. The continued application of our longitudinal cellular and molecular imaging tools will yield a better understanding of the mechanism(s) by which this physiological inhibition (CTR) and/or enhancement (CTE) occurs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".