Bevacizumab for Vestibular Schwannomas in Neurofibromatosis Type 2: A Systematic Review of Tumor Control and Hearing Preservation
Bibliographic record
Abstract
Background/Objectives: Vestibular schwannomas (VSs), also called acoustic neuromas, are benign tumors affecting the vestibulocochlear nerve, often leading to hearing loss and balance issues. This condition is particularly challenging in patients with neurofibromatosis type 2 (NF2), where VSs tend to develop bilaterally. Conventional treatments, such as surgery and radiotherapy, although effective, carry risks like hearing loss and nerve damage. Bevacizumab, a VEGF-targeting monoclonal antibody, has emerged as a less invasive treatment option, showing potential for tumor volume reduction and hearing preservation. This systematic review aims to assess the efficacy of bevacizumab in controlling tumor volume, preserving hearing, and identifying associated adverse events. Methods: A comprehensive systematic review was performed using PRISMA guidelines. PubMed and Cochrane Library databases were searched for studies evaluating the effects of bevacizumab on VS, focusing on key outcomes like tumor volume reduction, hearing preservation, and adverse events. Data extraction and quality assessment were independently conducted by two reviewers using the Newcastle-Ottawa Scale. Results: Nine studies involving 176 patients were included. Bevacizumab showed a partial tumor volume reduction (≥20%) in 40% of cases and disease stabilization in 50%, while 10% experienced tumor progression. Hearing outcomes revealed improvement in 36% of patients, stabilization in 46%, and deterioration in 18%. Severe adverse effects, including hypertension and thromboembolic events, occurred in 13% of patients, while 18% reported no side effects. Tumor regrowth was observed in some patients after treatment discontinuation, emphasizing the need for long-term monitoring. Conclusions: Bevacizumab demonstrates effectiveness in managing VS, particularly in NF2 patients, by reducing tumor size and preserving hearing in a substantial proportion of cases. However, the variability in patient response and the risk of adverse events underscore the need for individualized treatment approaches and further research, including randomized controlled trials, to optimize its clinical application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".