In Vivo Magnetic Resonance Imaging for Investigating the Development and Distribution of Experimental Brain Metastases due to Breast Cancer
Bibliographic record
Abstract
INTRODUCTION: The overall goal of this study was to assess the utility of three-dimensional magnetic resonance imaging (MRI) for monitoring the temporal and spatial development of experimental brain metastasis in mice. MATERIALS AND METHODS: Brain metastatic human breast cancer cells (231-BR or 231-BR-HER2) were injected intracardially in nude mice for delivery to the brain. Mouse brains were imaged in vivo at different time points using a balanced steady-state-free precession (bSSFP) pulse sequence at 1.5 T. Brains were categorized into four regions: cortex, central brain, olfactory, and posterior. The number of metastases and their volumes were quantified for both cell lines. RESULTS: There was no difference in the mean number of metastases for either cell line. The volumes of metastases in mice injected with 231-BR-HER2 cells were significantly larger than those for mice injected with 231-BR cells. The growth rate for 231-BR-HER2 metastases was 67.5% compared with 54.4% for the 231-BR metastases. More than 50% of metastases were located in the cortex and 25% to 30% of metastases were identified in the central brain for each time point and for mice injected with either cell line. The volumes of metastases were significantly larger in mice with fewer metastases at end point. SIGNIFICANT CONCLUSIONS: MRI provided a comprehensive accounting of the number and size of experimental brain metastases in the whole mouse brain at multiple time points. This approach has provided new information about the temporal and spatial development of metastases in the brain not possible by other histopathologic or imaging methods.
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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".