Imaging of Murine Brain Tumors Using a 1.5 Tesla Clinical MRI System
Bibliographic record
Abstract
BACKGROUND: In this study, we investigated the feasibility of using a 1.5 Tesla (T) clinical magnetic resonance imaging (MRI) system for in vivo assessment of three histopathologically different brain tumor models in mice. METHODS: We selected mouse models in which tumor growth was observed in different intracranial compartments: Patched+/- heterozygous knock-out mice for tumor growth in the cerebellum (n = 5); U87 MG human astrocytoma cells xenografted to the frontal lobe of athymic mice (n = 15); and F5 (n = 15) or IOMM Lee (n = 15) human malignant meningioma cells xenotransplanted to the athymic mouse skull base or convexity. Mice were imaged using a small receiver surface coil and a clinical 1.5 T MRI system. T1- and fast spin echo T2-weighted image sequences were obtained in all animals. Gadolinium was injected via tail vein to better delineate the intracranial tumors. Twenty mice were followed by serial MRI to study tumor growth over time. In these mice, images were typically performed after tumor implantation, and at two week intervals. Mice were euthanized following their last imaging procedure, and their tumors were examined by histopathology. The histopathological preparations were then compared to the last MR images to correlate the imaging features with the pathology. RESULTS: Magnetic resonance imaging delineated th tumors in the cerebellum, frontal lobes and skull base in all mouse models. The detection of intracranial tumors was enhanced with prio administration of gadolinium, and the limit of resolution of brain tumors in the mice was 1-2 mm3. Sequential images performed at different time intervals showed progressive tumor growth in all animals. The MR images of tumor size and location correlated accurately with th results of the histopathological analysis. CONCLUSION: Magnetic resonance imaging of murine brain tumors in different intracrania compartments is feasible with a 1.5 T clinical MR system and a specially designed surface coil. Tumors as small as 1-2 mm3 can be detecte with good image resolution. Mice harbouring nascent brain tumors can be followed sequentially by serial MR imaging. This may allow for a noninvasive means by which tumor growth can be measured, and novel therapies tested without resorting to sacrifice of the mice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".