Characterization of an Orthotopic Rat Model of Glioblastoma Using Multiparametric Magnetic Resonance Imaging and Bioluminescence Imaging
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most common primary brain tumor, with most patients dying within 15–18 months of diagnosis despite aggressive therapy. Preclinical GBM models are valuable for exploring GBM progression and for evaluating new therapeutics or imaging approaches. The rat C6 glioma model shares similarities with human GBM, and application of noninvasive imaging enables better study of disease progression. Here, multiparametric magnetic resonance imaging (mpMRI) and bioluminescence imaging (BLI) were applied to characterize longitudinal development of orthotopic luciferase-expressing C6 tumors. Across all rats (n = 11), a large variability was seen for BLI signal, a relative measure of C6 cell viability, but in most individuals, BLI signal peaked at day 11 and decreased thereafter. T2 and contrast-enhanced T1 tumor volumes significantly increased over time (P < .05), and volume measures did not correlate with BLI signal. After day 11, tumor regions of noncontrast enhancement appeared in postcontrast T1-weighted magnetic resonance imaging, and had significantly higher apparent diffusion coefficient values compared with contrast-enhanced regions (P < .05). This suggests formation of ill-perfused, necrotic regions beyond day 11, which were apparent at end-point–matched tissue sections. Our study represents the first combined use of BLI and mpMRI to characterize the progression of disease in the orthotopic C6 rat model, and it highlights the variability in tumor growth, the complementary information from BLI and mpMRI, and the value of multimodality imaging to better characterize tumor development. Future application of these imaging tools will be useful for evaluation of treatment response, and should be pertinent for other preclinical models.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".