Utility of Artificial Intelligence in Stereotactic Radiosurgery for Vestibular Schwannomas: A Systematic Review

Vestibular schwannomas (VSs) are benign neoplasms commonly located in the cerebellopontine angle and are increasingly managed with stereotactic radiosurgery (SRS), particularly Gamma Knife radiosurgery (GKRS). The integration of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) algorithms, into GKRS has emerged as a promising strategy to enhance diagnostic accuracy, automate treatment planning, and predict treatment response. This systematic review evaluates the current applications and clinical utility of AI in the stereotactic radiosurgical management of VSs. A systematic search was conducted on July 31, 2024, across Medline (PubMed), Embase, Scopus, and the Cochrane Library, in accordance with PRISMA guidelines. Studies were selected if they investigated the use of AI at any stage of stereotactic treatment or follow-up of VSs. Articles were excluded if they focused solely on microsurgical interventions or were review articles. Eligibility was independently assessed by two reviewers, with discrepancies resolved by a third observer. A total of 22 original studies were included in the final qualitative synthesis. AI applications were categorized into three domains: (1) pre-treatment tumor characterization and segmentation, (2) radiosurgical treatment planning, and (3) post-treatment response prediction. Multiple studies demonstrated the efficacy of convolutional neural networks (CNNs) and federated learning for automated and accurate segmentation of VSs, often achieving performance metrics comparable to expert manual annotations. In treatment planning, AI-driven models enabled improved target delineation, dosimetric optimization, and reduced inter-planner variability. In the post-treatment phase, radiomic-based AI models accurately predicted pseudoprogression and long-term tumor response, while automated volumetric assessment tools reliably tracked tumor changes over time. Collectively, these AI applications showed potential to streamline clinical workflows, enhance precision, and support individualized decision-making. AI has shown significant promise in enhancing various aspects of stereotactic radiosurgical care for VSs, from diagnosis and planning to longitudinal monitoring. While current findings are encouraging, challenges such as data standardization, model generalizability, and integration into clinical practice remain. Further prospective multicenter studies and regulatory oversight are warranted to validate AI tools and facilitate their widespread clinical adoption. With continued refinement, AI is likely to augment the capabilities of radiosurgeons and improve outcomes for patients with VS.