NIMG-31. RESIDUAL ENHANCING TUMOR VOLUME IS A STRONG PROGNOSTIC BIOMARKER FOR SURVIVAL IN BOTH NEWLY DIAGNOSED AND RECURRENT GBM REGARDLESS OF THERAPY: EVIDENCE FROM 1,535 PATIENTS IN SINGLE AND MULTICENTER TRIALS
Bibliographic record
Abstract
The prognostic significance of residual or baseline contrast enhancing tumor volume prior to initiation of chemotherapy in both newly diagnosed and recurrent GBM for OS remains controversial, particularly in the context of surgical resection and or use of anti-angiogenic therapy. In the current study we pooled 1,535 GBM patients, 1,038 newly diagnosed and 497 after 1-2 recurrences, from several single and multicenter chemotherapy and anti-angiogenic therapy clinical trials (AVAglio, BRAIN, and XL184-201). A central core lab quantified tumor volume using enhancing disease plus central necrosis from T1 subtraction maps to remove blood products and other artifacts. Data suggest that baseline tumor volume is a strong prognostic factor for OS in all therapeutic scenarios besides patients who previously failed anti-angiogenic therapy. In newly diagnosed GBM, a post-surgical residual enhancing tumor volume less than the median, 10mL (~1.34 cm diameter), was associated with a significant survival advantage independent of age and therapy (P<0.0001, HR=1.624, 95%C.I.=1.48-1.94). In recurrent GBM, a baseline tumor volume prior to 2nd or 3rd line therapy less than the median, 15mL (~1.53 cm in diameter), was associated with a significant survival advantage independent of age and therapy (P<0.0001, HR=1.773, 95%C.I.=1.48-2.23). Comparisons within treatment arms, molecular classifications, and methylation status will also be presented. Results support the hypothesis that contrast-enhancing tumor is a surrogate biomarker for active disease as evidenced by prognostic significance in almost every therapeutic context. Results suggest clinical trial treatment arms must have a balanced distribution of tumor size and tumor size should be considered when interpreting therapeutic efficacy.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".