Multicomponent T2 Analysis of Glioblastoma in a Mouse Model
Bibliographic record
Abstract
Glioblastoma Multiforme (GBM) is the most common and aggressive malignant primary brain tumour and average survival rates, even with aggressive treatment, is less than a year. A primary concern when imaging patients with tumors using MRI (Magnetic Resonance Imaging) is not being able to detect treatment response or tumor tissue characteristics adequately. MRI methods use the information obtained from the distribution of hydrogen protons from water-filled biological tissues. T2, the spin-spin relaxation time, is affected by the water environment and will increase with edema (excess water within tissues) and specific changes in cell type. Hence, unique T2 times reveal distinctive tissue characteristics. To date, T2 analysis of tumors has largely used monoexponential fitting. However, this method is not sensitive to the multicomponent nature of tissues within a defined volume (voxel). Using a mouse model implanted with tumor cells and multiexponential T2 analysis, superior differentiation between tissues can be detected within the mouse gliobastoma. We will use a novel visualization software to determine how the multicomponent T2 analysis can improve our sensitivity to specific tumor microenvironments. A study showing proof of principle has previously been published using mice implanted with patient-derived brain tumor initiating cells (BTICs). This project will use human derived glioblastoma cells in mice models to examine whether T2 can be used to detect treatment response. In addition MRI spectroscopy will be performed to detect the grade/type of tumor using the levels of metabolites present in and around the tumor volume.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".