通过在高级别神经胶质瘤中应用高分辨率 3D 7 特斯拉磁共振波谱成像 (MRSI)，我们之前发现了肿瘤内代谢异质性。在这项研究中，我们评估了 3D 7 T-MRSI 对神经胶质瘤级别和异柠檬酸脱氢酶 (IDH) 状态进行术前无创分类的潜力。我们证明 IDH 突变和神经胶质瘤分级可以通过超高场 (UHF) MRI 检测到。这项技术可能会优化神经胶质瘤患者的围手术期管理。我们前瞻性地纳入了 36 名 WHO 2021 2-4 级神经胶质瘤患者（20 名 IDH 突变型，16 名 IDH 野生型）。我们的 7 T 3D MRSI 序列提供了这些患者大脑的高分辨率代谢图（例如胆碱、肌酸、谷氨酰胺和甘氨酸）。我们对肿瘤分割内的体素采用多元随机森林和支持向量机模型，对神经胶质瘤分级和 IDH 突变状态进行分类。随机森林分析得出的基于代谢比的多元 IDH 分类的曲线下面积 (AUC) 为 0.86。我们通过总胆碱 (tCho)/总 N-乙酰天冬氨酸 (tNAA) 比率差异来区分高级别和低级别肿瘤，得出的 AUC 为 0.99。基于其他测量的代谢比率的肿瘤分类提供了相当的准确性。我们根据 7 T MRSI 和临床肿瘤分割，在术前成功地对 IDH 突变状态和高级别与低级别胶质瘤进行了分类。通过这种方法，我们证明了基于成像的肿瘤标志物预测至少与同类研究一样准确，突出了 MRSI 在术前肿瘤分类中的潜在应用。© 2024。作者。

With the application of high-resolution 3D 7 Tesla Magnetic Resonance Spectroscopy Imaging (MRSI) in high-grade gliomas, we previously identified intratumoral metabolic heterogeneities. In this study, we evaluated the potential of 3D 7 T-MRSI for the preoperative noninvasive classification of glioma grade and isocitrate dehydrogenase (IDH) status. We demonstrated that IDH mutation and glioma grade are detectable by ultra-high field (UHF) MRI. This technique might potentially optimize the perioperative management of glioma patients.We prospectively included 36 patients with WHO 2021 grade 2-4 gliomas (20 IDH mutated, 16 IDH wildtype). Our 7 T 3D MRSI sequence provided high-resolution metabolic maps (e.g., choline, creatine, glutamine, and glycine) of these patients' brains. We employed multivariate random forest and support vector machine models to voxels within a tumor segmentation, for classification of glioma grade and IDH mutation status.Random forest analysis yielded an area under the curve (AUC) of 0.86 for multivariate IDH classification based on metabolic ratios. We distinguished high- and low-grade tumors by total choline (tCho) / total N-acetyl-aspartate (tNAA) ratio difference, yielding an AUC of 0.99. Tumor categorization based on other measured metabolic ratios provided comparable accuracy.We successfully classified IDH mutation status and high- versus low-grade gliomas preoperatively based on 7 T MRSI and clinical tumor segmentation. With this approach, we demonstrated imaging based tumor marker predictions at least as accurate as comparable studies, highlighting the potential application of MRSI for pre-operative tumor classifications.© 2024. The Author(s).