Background/Objectives: The rapid development of artificial intelligence is transforming the face of medicine. Due to the large number of imaging studies (pre-, intra-, and postoperative) combined with histopathological and molecular findings, its impact may be particularly significant in neurosurgery. We aimed to perform a scoping review of recent applications of deep learning in MRI-based diagnostics of brain tumors relevant to neurosurgical practice.Methods: We conducted a systematic search of scientific articles available in the PubMed database. The search was performed on 22 April 2024, using the following query: ((MRI) AND (brain tumor)) AND (deep learning). We included original studies that applied deep-learning methods to brain tumor diagnostics using MRI, with potential relevance to neuroradiology or neurosurgery. A total of 893 records were retrieved, and after title/abstract screening and full-text assessment by two independent reviewers, 229 studies met the inclusion criteria. The study was not registered and received no external funding.Results: Most included articles were published after 1 January 2022. The studies primarily focused on developing models to differentiate between specific CNS tumors. With improved radiological analysis, deep-learning technologies can support surgical planning through enhanced visualization of cerebral vessels, white matter tracts, and functional brain areas. Over half of the papers (52%) focused on gliomas, particularly their detection, grading, and molecular characterization.Conclusions: Recent advancements in artificial intelligence methods have enabled differentiation between normal and abnormal CNS imaging, identification of various pathological entities, and, in some cases, precise tumor classification and molecular profiling. These tools show promise in supporting both diagnosis and treatment planning in neurosurgery.
背景/目的：人工智能的快速发展正在重塑医学领域的面貌。由于大量影像学检查（术前、术中及术后）与组织病理学及分子生物学发现相结合，其影响在神经外科领域可能尤为显著。本研究旨在对深度学习在基于磁共振成像的脑肿瘤诊断中与神经外科实践相关的最新应用进行范围综述。 方法：我们对PubMed数据库中收录的科学文献进行了系统性检索。检索于2024年4月22日执行，采用以下检索式：((MRI) AND (brain tumor)) AND (deep learning)。我们纳入了应用深度学习方法于脑肿瘤磁共振成像诊断、且与神经放射学或神经外科具有潜在相关性的原创性研究。共检索到893条记录，经两位独立评审者进行标题/摘要筛选及全文评估后，最终229项研究符合纳入标准。本研究未进行注册且未接受外部资助。 结果：大多数纳入文献发表于2022年1月1日之后。研究主要集中于开发区分特定中枢神经系统肿瘤的模型。通过改进的放射学分析，深度学习技术可通过增强脑血管、白质纤维束及功能脑区的可视化来辅助手术规划。超过半数（52%）的论文聚焦于胶质瘤，特别是其检测、分级及分子特征分析。 结论：人工智能方法的最新进展已能实现正常与异常中枢神经系统影像的区分、多种病理实体的识别，并在某些情况下完成精确的肿瘤分类与分子分型。这些工具在支持神经外科诊断与治疗规划方面展现出广阔前景。
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