Molecular susceptibility weighted imaging of the glioma rim in a mouse model
Bibliographic record
Abstract
BACKGROUND: Glioma is the most common and most difficult to treat brain cancer. Despite many efforts treatment, efficacy remains low. As neurosurgical removal is the standard procedure for glioma, a method, allowing for both early detection and exact determination of the location, size and extent of the tumor, could improve a patient's positive response to therapy. NEW METHOD: We propose application of susceptibility weighted molecular magnetic resonance imaging using, targeted contrast agents, based on superparamagnetic iron oxide nanoparticles, for imaging of the, glioma rim, namely brain-tumor interface. Iron oxide attached to the targeted cells increases, susceptibility differences at the boundary between tumor and normal tissue, providing the opportunity, to utilize susceptibility weighted imaging for improved tumor delineation. We investigated potential, enhancement of the tumor-brain contrast, including tumor core and rim when using susceptibility, weighted MRI for molecular imaging of glioma. RESULTS: There were significant differences in contrast-to-noise ratio before, 12 and 120min after contrast, agent injection between standard gradient echo pulse sequence and susceptibility weighted molecular, magnetic resonance imaging for the core-brain, tumor rim-core and tumor rim-brain areas. COMPARISON WITH EXISTING METHODS: Currently, the most common MRI contrast agent used for glioma diagnosis is a non-specific, gadolinium-based agent providing T1-weighted enhancement. Susceptibility-weighted magnetic, resonance imaging is much less efficient when no targeted superparamagnetic contrast agents are, used. CONCLUSION: The improved determination of glioma extent provided by SWI offers an important new tool for, diagnosis and surgical planning.
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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 | 0.007 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".