Microsurgical Resection Versus Stereotactic Radiosurgery for Trigeminal Schwannoma: A Meta-Analysis of 949 Patient Treatment Outcomes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Schwannomas are benign, slow-growing peripheral nerve sheath tumors. Approximately 8% of intracranial schwannomas originate from the trigeminal nerve. No consensus exists regarding superiority of outcomes after resection vs stereotactic radiosurgery (SRS) in the treatment of trigeminal schwannomas (TS). The aim of this study was to compare the efficacy and outcomes of resection vs SRS for TS management. METHODS: Embase, PubMed, and SCOPUS databases were queried from the date of inception to July 2023 for primary data reporting TS treatment outcomes after SRS or resection. Risk of bias was reported under Newcastle-Ottawa Scale. RESULTS: Overall, 29 retrospective observational studies comprising 949 patients were included in the analysis. A total of 13 studies (n = 589) reported outcomes after SRS, and 16 studies (n = 360) reported outcomes after surgical resection, and 182 patients in the SRS group had previous resection. To ensure statistical validity of comparing the cohorts, 3 checkpoints were assessed: the median age (SRS: 48 vs surgery: 40 years old, P < .01), tumor volume (5.2 vs 8.9 cm 3 , P = .06), and median follow-up (53.9 vs 48.5 months, P = .59), which reflected the choice of treatment. Compared with surgical resection, the SRS group demonstrated significantly higher rates of facial hypesthesia improvement (44% vs 12%, P < .01) and lower rates of new onset (4% vs 15%, P = .051) at last follow-up. Facial pain was less likely to improve (58% vs 81%, P = .024) after SRS compared with surgery. However, worsened (5% vs 1%, P = .71) and new facial pain (2% vs 1%, P = .55) were comparable between cohorts. CONCLUSION: SRS seems to offer improvement in rates of hypesthesia and trigeminal motor deficits compared with surgical resection for TS. However, facial pain improvement was more favorable after surgery. These findings highlight the importance of individualized treatment decisions based on patient characteristics, tumor profile, and pretreatment symptoms.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.034 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".