分子特征分析已成为原发性脑肿瘤分类和分级的重要诊断工具。分子标志物，如异柠檬酸脱氢酶（IDH）突变状态，1p/19q 缺失，甲基化的 O(6)-甲基鸟嘌呤-DNA 甲基转移酶（MGMT）启动子或 CDKN2A/B 纯合子缺失，可辨别不同的肿瘤实体和分级，并在治疗反应和预后中发挥关键作用。近年来，磁共振成像（MRI）的主要功能已经变成了检测肿瘤、为神经外科和放射治疗计划提供空间信息以及监测治疗反应。MRI 还显示了从基于影像的生物标志物评估胶质瘤分子特征的潜力。作为一个出色的例子，大量的研究已经证明 T2/FLAIR 不匹配征象能够以高达 100% 的特异性识别 IDH 突变、1p/19q 非缺失的星形细胞瘤。在其他情况下，多参数 MRI，通常与机器学习方法结合使用，似乎在预测分子标志物方面取得了最高的精确度。未来的相关应用可能是预测胶质瘤分子组成的变化，并提供有关胶质瘤细胞和基因异质性的有用信息，尤其是在未切除的肿瘤部位。

Molecular characterization has become a key diagnostic tool for the classification and grading of primary brain tumors. Molecular markers, such as isocitrate dehydrogenase (IDH) mutation status, 1p/19q codeletion, methylation of the O(6)-methylguanine-DNA methyltransferase (MGMT) promoter, or CDKN2A/B homozygous deletion discriminate different tumor entities and grades, and play a crucial role for treatment response and prognosis. In recent years, magnetic resonance imaging (MRI), whose main functions has been to detect a tumor, to provide spatial information for neurosurgical and radiotherapy planning, and to monitor treatment response, has shown potential in assessing molecular features of gliomas from image-based biomarkers. As an outstanding example, numerous studies have proven that the T2/FLAIR mismatch sign can identify IDH-mutant, 1p/19q non-codeleted astrocytomas with a specificity of up to 100%. For other purposes, multiparametric MRI, often coupled with machine learning methods, seems to achieve the highest accuracy in predicting molecular markers. Relevant future applications might be anticipating changes in the molecular composition of gliomas and providing useful information about the cellular and genetic heterogeneity of gliomas, especially in the non-resected tumor parts.