Facial Nerve Schwannoma Treatment with Stereotactic Radiosurgery (SRS) versus Resection followed by SRS: Outcomes and a Management Protocol
Bibliographic record
Abstract
Abstract Background Stereotactic radiosurgery (SRS) and resection are treatment options for patients with facial nerve schwannomas without mass effect. Objective This article evaluates outcomes of patients treated with SRS versus resection + SRS. Method We retrospectively compared 43 patients treated with SRS to 12 patients treated with resection + SRS. The primary study outcome was unfavorable combined endpoint, defined as worsening or new clinical symptoms, and/or tumor radiological progression. SRS (38.81 ± 5.3) and resection + SRS (67.14 ± 11.8) groups had similar clinical follow-ups. Results At the time of SRS, the tumor volumes of SRS (mean ± standard error; 1.83 ± 0.35 mL) and resection + SRS (2.51 ± 0.75 mL) groups were similar. SRS (12.15 ± 0.08 Gy) and resection + SRS (12.16 ± 0.14 Gy) groups received similar radiation doses. SRS group (42/43, 98%) had better local tumor control than the resection + SRS group (10/12, 83%, p = 0.04). Most of SRS (32/43, 74%) and resection + SRS (10/12, 83%) group patients reached a favorable combined endpoint following SRS (p = 0.52). Considering surgical associated side effects, only 2/10 patients of the resection + SRS group reached a favorable endpoint (p < 0.001). Patients of SRS group, who are > 34 years old (p = 0.02), have larger tumors (> 4 mL, 0.04), internal auditory canal (IAC) segment tumor involvement (p = 0.01) were more likely to reach an unfavorable endpoint. Resection + SRS group patients did not show such a difference. Conclusion While resection is still needed for larger tumors, SRS offers better clinical and radiological outcomes compared to resection followed by SRS for facial schwannomas. Younger age, smaller tumors, and non-IAC situated tumors are factors that portend a favorable outcome.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".