放射基因组分析为神经胶质瘤提供了有价值的成像生物标志物和生物学见解。 DNA甲基化等分子谱的放射基因组标记物仍有待发现，以协助分子诊断和肿瘤治疗。我们应用机器学习方法来识别与 146 名胶质瘤患者的分子谱相关的磁共振成像 (MRI) 特征，并开发并验证了每个分子特征 (MoRad) 的拟合模型。为了为分子谱提供放射学注释，我们设计了两种新方法，称为放射组学肿瘤学 (RO) 和放射组学集富集分析 (RSEA)。生成的 MoRad 模型在分析每个分子特征的放射组学特征方面表现良好，包括突变、甲基化、转录和蛋白质概况。其中，MoRad模型在定量绘制全球DNA甲基化方面表现出色。通过 RO 和 RSEA 方法，我们发现全局 DNA 甲基化可以通过 T2 加权 MRI 增强区域的体积和纹理特征的异质性来反映。最后，我们证明了整体 DNA 甲基化与临床病理学、分子和免疫学特征的关联，包括组织学分级、IDH 和 ATRX 突变、MGMT 甲基化、多种甲基化高亚型、肿瘤浸润淋巴细胞和长期生存结果。整体 DNA 甲基化与神经胶质瘤的放射学特征高度相关。放射基因组全局甲基化是一种基于成像的定量分子生物标志物，与特定的共识分子亚型和免疫特征相关。© 2024。作者。

The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment.We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA).The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes.Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.© 2024. The Author(s).