Machine learning-based models in prediction of the radiological outcomes of vestibular schwannoma following stereotactic radiosurgery: a systematic review and meta-analysis.

ABSTRACT:

Background

Prediction of the radiological outcomes of the vestibular schwannomas (VSs) following stereotactic radiosurgery (SRS) is critical in the management of these lesions. Predictions of tumor control can optimize therapeutic strategies and enhance treatment outcomes. Significant advancements in machine learning (ML) have led to the development of models to predict the radiological outcomes after SRS in VS individuals. This study evaluated the role of ML-based models in predicting the radiological outcomes of SRS in the setting of VS.

Methods

On December 12, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated the performance outcomes of the ML-based predictive models were included. The poo

SUBMITTER: Hajikarimloo B 

PROVIDER: S-EPMC12465916 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature