Kurokawa Ryo, Hagiwara Akifumi, Ueda Daiju, Ito Rintaro, Saida Tsukasa, Honda Maya, Nishioka Kentaro, Sakata Akihiko, Yanagawa Masahiro, Takumi Koji, Oda Seitaro, Ide Satoru, Sofue Keitaro, Sugawara Shunsuke, Watabe Tadashi, Hirata Kenji, Kawamura Mariko, Iima Mami, Naganawa Shinji
Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
Radiol Med. 2025 Aug 25. doi: 10.1007/s11547-025-02078-9.
Recent advances in molecular genetics have revolutionized the classification of pediatric-type high-grade gliomas in the 2021 World Health Organization central nervous system tumor classification. This narrative review synthesizes current evidence on the following four tumor types: diffuse midline glioma, H3 K27-altered; diffuse hemispheric glioma, H3 G34-mutant; diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype; and infant-type hemispheric glioma. We conducted a comprehensive literature search for articles published through January 2025. For each tumor type, we analyze characteristic clinical presentations, molecular alterations, conventional and advanced magnetic resonance imaging features, radiological-molecular correlations, and current therapeutic approaches. Emerging radiogenomic approaches utilizing artificial intelligence, including radiomics and deep learning, show promise in identifying imaging biomarkers that correlate with molecular features. This review highlights the importance of integrating radiological and molecular data for accurate diagnosis and treatment planning, while acknowledging limitations in current methodologies and the need for prospective validation in larger cohorts. Understanding these correlations is crucial for advancing personalized treatment strategies for these challenging tumors.
分子遗传学的最新进展彻底改变了2021年世界卫生组织中枢神经系统肿瘤分类中儿童型高级别胶质瘤的分类。这篇叙述性综述综合了以下四种肿瘤类型的现有证据：弥漫性中线胶质瘤，H3 K27改变型；弥漫性半球胶质瘤，H3 G34突变型；弥漫性儿童型高级别胶质瘤，H3野生型和异柠檬酸脱氢酶（IDH）野生型；以及婴儿型半球胶质瘤。我们对截至2025年1月发表的文章进行了全面的文献检索。对于每种肿瘤类型，我们分析了其特征性临床表现、分子改变、传统和先进的磁共振成像特征、放射学与分子的相关性以及当前的治疗方法。利用人工智能的新兴放射基因组学方法，包括放射组学和深度学习，在识别与分子特征相关的成像生物标志物方面显示出前景。本综述强调了整合放射学和分子数据以进行准确诊断和治疗规划的重要性，同时承认当前方法的局限性以及在更大队列中进行前瞻性验证的必要性。了解这些相关性对于推进这些具有挑战性肿瘤的个性化治疗策略至关重要。
