A Retrospective Study on Prognostic Factors and Systemic Treatments of Refractory Meningiomas
Bibliographic record
Abstract
Standard systemic treatment has not been established for refractory meningioma. This retrospective study aimed to identify prognostic factors for overall survival and document outcomes of systemic therapies. We reviewed patients with meningioma followed at CHUM hospital between 2006 and 2022. Only patients with progression after first-line treatment were included. Among 750 patients, 107 (14%) experienced progression after first-line treatment. They were divided into two groups: Group 1 (n = 69, 64%) received salvage local treatments, and Group 2 (n = 38, 36%) received additional salvage systemic treatments. The median follow-up time from diagnosis was 7.5 years. 10-year OS was 88.3% (Group 1) vs. 67.2% (Group 2) (p = 0.009). Mean survival after stopping systemic treatment was 8.94 months. Key prognostic factors for poorer survival included age ≥ 65 (HR = 2.82; p = 0.009), WHO grade 2 or 3 (HR = 4.25; p = 0.004), and progression after second-line treatment (HR = 4.77; p = 0.004). Bevacizumab was associated with a mPFS of 12 months and 1-year OS of 64,6%, whereas non-Bevacizumab treatments—including Hydroxyurea, Somatostatin, and Sunitinib—were associated with a mPFS of 7 months and 1-year OS of 52,6%. This study highlights the fatal nature of recurrent meningiomas and the urgent need for systemic treatments that can improve their survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".