P14.92 Study of Glutaminergic and Glutamatergic Metabolism in 1H-MRS Monovoxel in the Most Aggressive Part of 62 Glioblastoma Before and After 18 months Treatment
Bibliographic record
Abstract
Abstract BACKGROUND To study the relationships between glutaminergic metabolism (Glx/tCr), tumor proliferation (tCho/tCr) and other metabolic activities in patients with glioblastoma (GBM). MATERIAL AND METHODS Patients: 62 patients with glioblastoma, all having a STUPP Protocol (radiotherapy and concomitant chemotherapy), were selected and separated into 2 groups: Biopsies (30) and resections (32). In total, 269 NMR spectra (PRESS at GE 1.5T and 3T; multi-TEs TE=35ms and TE=144ms) were acquired. Processing: MRS data were processed with jMRUI software and quantitated using HLSVD and QUEST algorithms. Statistical analysis of longitudinal MRS data (every 3 months) RESULTS Glx/tCr and Lac/tCr ratios are correlated with the tumoral proliferation (tCho/tCr) before the beginning of treatment. This correlations decreases over time in biopsied and resected patients. In biopsied patients, the evolution of lactate (Lac/tCr) and Glx (Glx/tCr) ratios is similar along the follow-up with a progressive decrease in tumor proliferation (tCho/tCr). However, in resected patients, the evolution of lactate (Lac/tCr) and Glx (Glx/tCr) ratios is similar until 6 months and differ above: a progressive decrease of Lac/tCr and Glx/tCr until 18 months with a higher level of Glx/tCr. CONCLUSION Despite the difficulties to separate glutamine from glutamate (post-processing improvement is ongoing), spectroscopic measurements of Glx changes before clinical deterioration. The increase of Glx is longer (in time) than the Lactate increase after 6 months of treatment in the resected patients could be predictive of the observed increase of tumor proliferation at 12 months of treatment.The study of glutaminergic metabolism in the GBM could be used to evaluate the response to treatment. Being able to predict the increase of tumor proliferation in resected patients could allow a faster treatment adaptation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".