Comparing the Fate of Brain Metastatic Breast Cancer Cells in Different Immune Compromised Mice with Cellular Magnetic Resonance Imaging
Bibliographic record
Abstract
Abstract Metastasis is the leading cause of mortality in breast cancer patients, with brain metastases becoming increasingly prevalent. Studying this disease is challenging due to the limited experimental models and methods available. Here, we used iron-based cellular MRI to track the fate of a mammary carcinoma cell line (MDA-MB-231-BR) in vivo to characterize the growth of brain metastases in the nude and severely immune-compromised NOD/SCID/ILIIrg−/− (NSG) mouse. Nude and NSG mice received injections of iron-labeled MDA-MB-231-BR cells. Images were acquired with a 3T MR system and assessed for signal voids and metastases. The percentage of signal voids and the number and volume of metastases were quantified. Ex vivo imaging of the liver, histology, and immunofluorescence labeling was performed. On day 0, iron-labeled cells were visualized as signal voids throughout the brain. The percentage of voids decreased significantly between day 0 and endpoint. At endpoint, there was no difference in the number of brain metastases or tumour burden in NSG mice compared to nudes. Tumour volumes in nude mice were significantly larger than in NSG mice. Body images indicated that the NSG mice had metastases in the liver, lungs, and lymph nodes. Characterization of the NSG and nude mouse is necessary to study breast cancer brain metastasis in vivo . Here, we show that the 231BR cell line grew differently in NSG mice compared to nude mice. This work demonstrates the role that imaging can play toward credentialing these models that cannot be done with other in vitro or histopathologic methods alone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".