神经胶质瘤和胶质母细胞瘤是中枢神经系统 (CNS) 肿瘤的重要组成部分，与高死亡率和可变的预后相关。 2021年，世界卫生组织（WHO）更新了胶质瘤分类标准，最引人注目的是，将包括CDKN2A/B纯合缺失、TERT启动子突变、EGFR扩增、 7/-10染色体拷贝数变化等分子标记纳入分级和分类。成人和儿童神经胶质瘤的分类。这些标志物的纳入以及相应的新神经胶质瘤亚型的引入，使得临床干预措施的调整变得更加具体，并激发了新一波放射基因组学研究，寻求利用医学成像信息来探索这些新生物标志物的诊断和预后影响。放射组学、深度学习和组合方法使得强大的计算工具得以开发，用于 MRI 分析，将成像特征与纳入更新的 WHO CNS-5 指南的各种分子生物标志物相关联。最近的研究利用这些方法，根据这些更新的仅基于非侵入性 MRI 的分子标准对胶质瘤进行准确分类，证明了放射基因组学工具的巨大前景。在这篇综述中，我们探讨了这些计算框架的相对优点和缺点，并强调了最近在快速发展的基于分子的胶质瘤亚型分型领域的研究所带来的技术和临床创新。此外，还强调了将这些工具纳入常规放射工作流程的潜在好处和挑战，旨在加强患者护理并优化不断发展的中枢神经系统肿瘤管理领域的临床结果。© 2024。作者。

Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates and variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/-10 chromosome copy number changes, and others into the grading and classification of adult and pediatric Gliomas. The inclusion of these markers and the corresponding introduction of new Glioma subtypes has allowed for more specific tailoring of clinical interventions and has inspired a new wave of Radiogenomic studies seeking to leverage medical imaging information to explore the diagnostic and prognostic implications of these new biomarkers. Radiomics, deep learning, and combined approaches have enabled the development of powerful computational tools for MRI analysis correlating imaging characteristics with various molecular biomarkers integrated into the updated WHO CNS-5 guidelines. Recent studies have leveraged these methods to accurately classify Gliomas in accordance with these updated molecular-based criteria based solely on non-invasive MRI, demonstrating the great promise of Radiogenomic tools. In this review, we explore the relative benefits and drawbacks of these computational frameworks and highlight the technical and clinical innovations presented by recent studies in the landscape of fast evolving molecular-based Glioma subtyping. Furthermore, the potential benefits and challenges of incorporating these tools into routine radiological workflows, aiming to enhance patient care and optimize clinical outcomes in the evolving field of CNS tumor management, have been highlighted.© 2024. The Author(s).