Imaging iron‐loaded mouse glioma tumors with bSSFP at 3 T
Bibliographic record
Abstract
The poor prognosis for patients with high-grade glioma is partly due to the invasion of tumor cells into surrounding brain tissue. The goal of the present work was to develop a mouse model of glioma that included the potential to track cell invasion using MRI by labeling GL261 cells with iron oxide contrast agents prior to intracranial injection. Two types of agents were compared with several labeling schemes to balance between labeling with sufficient iron to curb the dilution effect of cell division while avoiding overwhelming signal loss that could prevent adequate visualization of tumor boundaries. The balanced steady-state free precession (bSSFP) pulse sequence was evaluated for its suitability for imaging glioma tumors and compared to T(2)-weighted two-dimensional fast spin echo (FSE) and T(1)-weighted spoiled gradient recalled echo (SPGR) at 3 T in terms of signal-to-noise ratio and contrast-to-noise ratio efficiencies. Ultimately, a three-dimensional bSSFP protocol consisting of a set of two images with complementary contrasts was developed, allowing excellent tumor visualization with minimal iron contrast when using pulse repetition time = 6 ms and alpha = 40 degrees, and extremely high sensitivity to iron when using pulse repetition time = 22 ms and alpha = 20 degrees. Quantitative histologic analysis validated that the strong signal loss seen in balanced steady state free precession pulse sequence images of iron-loaded tumors correlated well with the presence of iron.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".