T1ρ$$ {}_{1\rho } $$ as a Biomarker for IDH1 Mutation Status in a Glioma Mouse Model
Bibliographic record
Abstract
ABSTRACT Glioma is a common and often aggressive malignant brain cancer for which treatment is in part dependent on the mutation status of the IDH gene. Current diagnostic methods require a biopsy and genetic analysis to obtain IDH status, which can have lengthy wait times. T, the spin–lattice relaxation in the rotating frame, has shown potential to serve as a faster, non‐invasive means of IDH1‐typing that could be implemented at clinically relevant field strengths; however, there have been few studies to date that have explored its utility. This study consisted of three groups of five mice: naïve controls, IDH1‐wild‐type glioma bearing and IDH1‐mutant glioma bearing, imaged once weekly with a T‐prepped EPI sequence. It was found that IDH1‐mutant gliomas exhibited significantly higher T values in the tumour compared with the brain, while IDH1‐wild‐type gliomas presented similar T values in the tumour and brain. T was found to be sensitive to whole‐brain changes linked to glioma and IDH status, although further investigations into the confounding effects related to T and T differences are needed. A measurement of tumour T normalised to the brain (T) was able to distinguish between IDH1‐mutant and ‐wild‐type glioma, with IDH1‐mutant mice exhibiting an average T of 29% and IDH1‐wild‐type mice having an average T of only 3%. T may thus have the capacity to serve as a non‐invasive biomarker for IDH1 typing.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".