P.044 Prospective clinical detection of 2-hydroxyglutarate to predict IDH-mutant gliomas using magnetic resonance spectroscopy: preliminary results
Bibliographic record
Abstract
Background: With the advent of the 2016 WHO classification of tumours, prognostically distinct subclasses of glioma have been revealed. A subset of gliomas which harbor the isocitrate dehydrogenase (IDH) mutation have a survival advantage. 2-Hydroxyglutarate (2-HG) is a byproduct of faulty IDH metabolism in IDH mutants making it an ideal tumour biomarker. Since pre-operative detection of this metabolite using magnetic resonance spectroscopy (MRS) may yield valuable information for the neurosurgeon, we undertook the first Canadian utility study to detect 2-HG via MRS. Methods: We will recruit 150 patients presenting with a newly suspected glioma. All patients will undergo MRS scans for 2-HG pre-operatively and the neuropathologist will determine IDH status post-operatively based on immunohistochemistry and DNA sequencing. Pre-operative detection of 2-HG will be compared to post-operative IDH status. Results: To date, of 34 eligible subjects, 29 have glioma determined by pathology. Seven of these were IDH-mutant positive by pathology, of which 3 were detected by MRS. One glioma positive for 2-HG on MRS turned out to be IDH mutant negative on pathology. Conclusions: Prospective detection of 2-HG via MRS is feasible in the clinical setting. Additional subjects as well as refinement of our MRS protocol may yield higher sensitivity and specificity of this novel and clinically relevant diagnostic tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".