Integrating artificial intelligence with Gamma Knife radiosurgery in treating meningiomas and schwannomas: a review

Meningiomas and schwannomas are benign tumors that affect the central nervous system, comprising up to one-third of intracranial neoplasms. Gamma Knife radiosurgery (GKRS), or stereotactic radiosurgery (SRS), is a form of radiation therapy. Although referred to as "surgery," GKRS does not involve incisions. The GK medical device effectively utilizes highly focused gamma rays to treat lesions or tumors, primarily in the brain. In radiation oncology, machine learning (ML) has been used in various aspects, including outcome prediction, quality control, treatment planning, and image segmentation. This review will showcase the advantages of integrating artificial intelligence with Gamma Knife technology in treating schwannomas and meningiomas.This review adheres to PRISMA guidelines. We searched the PubMed, Scopus, and IEEE databases to identify studies published between 2021 and March 2025 that met our inclusion and exclusion criteria. The focus was on AI algorithms applied to patients with vestibular schwannoma and meningioma treated with GKRS. Two reviewers participated in the data extraction and quality assessment process.A total of nine studies were reviewed in this analysis. One distinguished deep learning (DL) model is a dual-pathway convolutional neural network (CNN) that integrates T1-weighted (T1W) and T2-weighted (T2W) MRI scans. This model was tested on 861 patients who underwent GKRS, achieving a Dice Similarity Coefficient (DSC) of 0.90. ML-based radiomics models have also demonstrated that certain radiomic features can predict the response of vestibular schwannomas and meningiomas to radiosurgery. Among these, the neural network model exhibited the best performance. AI models were also employed to predict complications following GKRS, such as peritumoral edema. A Random Survival Forest (RSF) model was developed using clinical, semantic, and radiomics variables, achieving a C-index score of 0.861 and 0.780. This model enables the classification of patients into high-risk and low-risk categories for developing post-GKRS edema.AI and ML models show great potential in tumor segmentation, volumetric assessment, and predicting treatment outcomes for vestibular schwannomas and meningiomas treated with GKRS. However, their successful clinical implementation relies on overcoming challenges related to external validation, standardization, and computational demands. Future research should focus on large-scale, multi-institutional validation studies, integrating multimodal data, and developing cost-effective strategies for deploying AI technologies.