Stereotactic Radiosurgery Versus Active Surveillance for Asymptomatic, Skull-Based Meningiomas: An International, Multicenter Matched Cohort Study
Bibliographic record
Abstract
Abstract Objective: The optimal management of asymptomatic, skull-based meningiomas is not well defined. The aim of this study is to compare the imaging and clinical outcomes of patients with asymptomatic, skull-based meningiomas managed either with upfront stereotactic radiosurgery (SRS) or active surveillance. Methods: This retrospective, multicenter study involved patients with asymptomatic, skull-based meningiomas. The study end-points included local tumor control and the development of new neurological deficits attributable to the tumor. Factors associated with tumor progression and neurological morbidity were also analyzed. Results: The combined unmatched cohort included 417 patients. Following propensity score matching for age, tumor volume, and follow-up 110 patients remained in each cohort. Tumor control was achieved in 98.2% and 61.8% of the SRS and active surveillance cohorts, respectively. SRS was associated with superior local tumor control (p<0.001, HR=0.01, 95% CI=0.002-0.13) compared to active surveillance. Three patients (2.7%) in the SRS cohort and six (5.5%) in the active surveillance cohort exhibited neurological deterioration. One (0.9 %) patient in the SRS-treated and 11 (10%) patients in the active surveillance cohort required surgical management of their meningioma during follow-up. Conclusions: SRS is associated with superior local control of asymptomatic, skull-based meningiomas as compared to active surveillance and does so with low morbidity rates. Active surveillance with regular neuroimaging studies does not always detect tumor growth before symptomatic progression. SRS should be considered as an alternative to active surveillance at diagnosis of an asymptomatic skull base meningioma. If active surveillance is initially chosen, SRS should be recommended when tumor growth from the original presenting volume is noted during follow-up.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".