Baseline pretreatment contrast enhancing tumor volume including central necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials
Bibliographic record
Abstract
BACKGROUND: The prognostic significance of baseline contrast enhancing tumor prior to second- or third-line therapy in recurrent glioblastoma (GBM) for overall survival (OS) remains controversial, particularly in the context of repeated surgical resection and/or use of anti-angiogenic therapy. In the current study, we examined recurrent GBM patients from both single and multicenter clinical trials to test whether baseline enhancing tumor volume, including central necrosis, is a significant prognostic factor for OS in recurrent GBM. METHODS: Included were 497 patients with recurrent GBM from 4 data sources: 2 single-center sites (University of Toronto, University of California Los Angeles) and 2 phase II multicenter trials (AVF3708G, Bevacizumab ± Irinotecan, NCT00345163; XL184-201, Cabozantinib, NCT00704288). T1 subtraction maps were used to define volume of contrast enhancing tumor, including central necrosis. Cox multivariable and univariate analyses were used to evaluate the relationship between tumor volume prior to second- or third-line therapy and OS. RESULTS: Both continuous measures of baseline tumor volume and tumors dichotomized into large (≥15cc) and small (<15cc) tumors were significant predictors of OS (P<.0001), independently of age and treatment. Univariate analysis demonstrated significant OS differences (P<.05) between large (≥15cc) and small (<15cc) tumors in patients under all therapeutic scenarios. Only patients treated with cabozantinib who previously failed anti-angiogenic therapy did not show an OS dependence on baseline tumor volume. CONCLUSIONS: Baseline tumor volume is a significant prognostic factor in recurrent GBM. 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 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 | 0.006 |
| 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".