NIMG-27. THRESHOLD VALUES OF EARLY METABOLIC CHANGES IN 1H MRSI METABOLIC METRICS PREDICTIVE FOR SURVIVAL IN PATIENTS WITH NEWLY DIAGNOSED HIGH-GRADE GLIOMA
Bibliographic record
Abstract
Abstract BACKGROUND The purpose of this study was to evaluate the association of specific threshold values for changes in metabolic metrics measured from 1H magnetic resonance spectroscopic imaging (MRSI) to survival of patients with high-grade glioma treated with multimodality therapy. METHODS Forty-four patients with newly diagnosed high-grade glioma were prospectively enrolled. Serial MRI and MRS scans were performed at baseline, week 4 of radiation therapy (RT), and two months post-RT to measure tumor choline, creatine, and N-acetylaspartate (NAA) normalized to contralateral normal brain. Serial changes in metabolites were assessed using the Wilcoxon signed-rank test. Cox regression analyses adjusted for patient age, KPS, and delivery of concurrent chemotherapy were used to assess the association of changes in metabolic metrics with survival. RESULTS Median follow-up time was 13.4 years, and vital status was available for 95.1% of patients. Mean normalized choline, creatine and choline/creatine decreased significantly after baseline to post-RT in the overall group of patients (all P < 05). Overall survival (OS) was longer in patients with ≤ 20% increase (vs. > 20%) in normalized choline (P = .024) or choline/NAA (P = .024) from baseline to week 4 of RT. During this period, progression-free survival (PFS) was longer in with patients ≤ 40% increase (vs. > 40%) in normalized choline (P = .013). From mid-RT to post-RT, ≤ 40% decrease (vs. > 40%) in normalized choline/NAA (P < .001) or choline/creatine (P = .014), or ≤ 40% increase (vs. > 40%) in NAA/creatine (P = .030) were associated with reduced median OS. CONCLUSIONS Threshold values for serial changes in choline metrics on MRSI associated with OS and PFS were identified. These findings suggest a potential clinical role for MRSI to provide an early assessment of treatment response, and could enable risk-adapted therapy in clinical trial development and clinical practice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".