Alhosanie Tasneem N, Hammo Bassam, Klaib Ahmad F, Alshudifat Abdulrahman
Software Engineering Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan.
King Abdullah II School of Information Technology, The University of Jordan, Amman, 11942, Jordan.
Neurosurg Rev. 2025 Sep 18;48(1):655. doi: 10.1007/s10143-025-03820-7.
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.
脑膜瘤和神经鞘瘤是影响中枢神经系统的良性肿瘤，占颅内肿瘤的三分之一。伽玛刀放射外科手术（GKRS），即立体定向放射外科手术（SRS），是一种放射治疗形式。尽管被称为“手术”，但GKRS并不涉及切口。GK医疗设备有效地利用高度聚焦的伽马射线来治疗病变或肿瘤，主要是脑部的病变或肿瘤。在放射肿瘤学中，机器学习（ML）已应用于各个方面，包括结果预测、质量控制、治疗计划和图像分割。本综述将展示将人工智能与伽马刀技术相结合治疗神经鞘瘤和脑膜瘤的优势。本综述遵循PRISMA指南。我们在PubMed、Scopus和IEEE数据库中进行搜索，以识别2021年至2025年3月期间发表的符合我们纳入和排除标准的研究。重点是应用于接受GKRS治疗的前庭神经鞘瘤和脑膜瘤患者的人工智能算法。两名评审员参与了数据提取和质量评估过程。本分析共审查了九项研究。一种著名的深度学习（DL）模型是双通路卷积神经网络（CNN），它整合了T1加权（T1W）和T2加权（T2W）磁共振成像扫描。该模型在861例接受GKRS治疗的患者身上进行了测试，获得了0.90的骰子相似系数（DSC）。基于ML的放射组学模型也表明，某些放射组学特征可以预测前庭神经鞘瘤和脑膜瘤对放射外科手术的反应。其中，神经网络模型表现出最佳性能。人工智能模型还被用于预测GKRS后的并发症，如瘤周水肿。使用临床、语义和放射组学变量开发了一种随机生存森林（RSF）模型，C指数得分分别为0.861和0.780。该模型能够将患者分为GKRS后发生水肿的高风险和低风险类别。人工智能和机器学习模型在接受GKRS治疗的前庭神经鞘瘤和脑膜瘤的肿瘤分割、体积评估和预测治疗结果方面显示出巨大潜力。然而，它们在临床上的成功实施依赖于克服与外部验证、标准化和计算需求相关的挑战。未来的研究应侧重于大规模、多机构验证研究，整合多模态数据，并制定具有成本效益的人工智能技术部署策略。
