Nonconventional Imaging Modalities for Vestibular Schwannoma Prognosis: A Scoping Review
Abstract
OBJECTIVE: To explore the existing evidence on novel imaging modalities that may aid in prognostication of vestibular schwannoma (VS) clinical outcomes across various management approaches. STUDY DESIGN: Scoping review. SETTING: Not applicable. PATIENTS: Individuals diagnosed with VS. INTERVENTIONS: Novel imaging techniques. MAIN OUTCOME MEASURES: Tumor growth and response to medical therapy. RESULTS: A total of 18 articles were included in this review. Of these, 7 focused on pretreatment prognosis, 6 evaluated radiosurgery outcomes, 4 examined microsurgery outcomes, and 1 assessed response to bevacizumab. The imaging modalities investigated included radiomics (n = 10), positron emission tomography (PET; n = 2), dynamic contrast-enhanced MRI (DCE-MRI; n = 2), both PET and DCE-MRI (n = 1), magnetic resonance elastography (MRE; n = 1), contrast-enhanced CT (CE-CT; n = 1), and slip interface imaging (SII; n = 1). Radiomic studies yielded encouraging results in predicting tumor growth after wait-and-scan and radiosurgery, with reported accuracies ranging from 52% to 88%, sensitivities from 13% to 95%, specificities from 50% to 94%, and area under the curves from 0.65 to 0.99. PET studies identified several promising ligands associated with tumor growth during wait-and-scan, suggesting a potential role in prognostication. DCE-MRI also demonstrated associations with tumor growth and treatment response to bevacizumab. Studies on MRE, CE-CT, and SII identified novel imaging parameters associated with tumor stiffness and adhesion. CONCLUSIONS: Several emerging imaging modalities hold potential for improving clinical outcome prognostication in VS. Techniques such as radiomics, PET, and DCE-MRI have shown promising results; however, the supporting evidence is still limited and heterogeneous. Further research is necessary to validate and refine these approaches to enhance their clinical utility.
Fields of Study & Subjects
Meningioma and schwannoma management
Radiomics and Machine Learning in Medical Imaging
Pituitary Gland Disorders and Treatments
Publication details
- Journal / source
- Otology & Neurotology
- Volume
- 47
- Issue
- 5
- Pages
- 649–656
- Publication date
- 13 February 2026
- Language
- EN