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The growth rate of sporadic VS varies considerably, posing challenges for consistent clinical management. This systematic review examines data on factors associated with VS growth, following a protocol registered in the PROSPERO database. The analysis reveals that key predictors of tumor growth include tumor location, initial size, and specific clinical symptoms such as hearing loss and imbalance. Additionally, several studies suggest that growth observed within the first year may serve as an indicator of subsequent progression, enabling the earlier identification of high-risk cases. Emerging factors such as the posture swing test and MRI signal intensity have also been identified as novel predictors that could further refine growth assessments. Our meta-analysis confirms that tumor location, initial size, cystic components, and vestibular symptoms are closely linked to the likelihood of VS growth. This review provides valuable guidance for clinicians in identifying patients who may require closer monitoring or early intervention. By integrating these predictive factors into clinical practice, this review supports more personalized treatment and contributes to the development of more accurate prognostic models for managing untreated sporadic VS.

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase ( IDH )-wild type GBM. Predictive models for IDH -mutation, 06-methylguanine-DNA-methyltransferase ( MGMT )-methylation and epidermal growth factor receptor ( EGFR ) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64-0.78) and used to stratify Kaplan-Meijer curves in two survival groups ( p -value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582-8.25) and MGMT -methylation (AUC 0.667, 95% CI 0.522-0.82) but not for IDH -mutation (AUC 0.695, 95% CI 0.436-0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.