尽管进行了最大程度的安全切除和辅助放化疗，高级别胶质瘤的复发仍是不可避免的，并且当前的成像技术无法预测未来的进展。然而，我们引入了一种新颖的全脑磁共振波谱 (WB-MRS) 方案，该方案深入研究肿瘤微环境的复杂性，提供对神经胶质瘤进展的全面了解，为预期手术和辅助干预提供信息。我们研究了五种局部肿瘤代谢物治疗后人群和应用机器学习 (ML) 技术来分析七个感兴趣区域内的关键关系：对侧正常外观的白质 (NAWM)、液体衰减反转恢复 (FLAIR)、WB 时的对比增强肿瘤MRS（肿瘤）、未来复发区域 (AFR)、全脑健康 (WBH)、非进展性 FLAIR (NPF) 和进展性 FLAIR (PF)。开发、优化、训练、测试和验证了五个监督机器学习分类模型和一个神经网络。最后，开发了一个网络应用程序来托管我们的新型计算器，迈阿密胶质瘤预测图 (MGPM)，用于开源交互。本研究纳入了 16 名在 WB-MRS 之前经组织病理学证实为高级别胶质瘤的患者，总计118,922 个全脑体素。机器学习模型成功地将正常的白质与肿瘤和未来的进展区分开来。值得注意的是，性能最高的 ML 模型在治疗后环境中的液体衰减反转恢复 (FLAIR) 信号中预测了神经胶质瘤的进展（平均 AUC = 0.86），其中 Cho/Cr 是最重要的特征。这项研究标志着一个重要的里程碑：同类中第一个在发现后 8 个月内揭示治疗后神经胶质瘤的放射学隐匿性神经胶质瘤进展。这些发现强调了基于机器学习的 WB-MRS 生长预测的实用性，为指导早期治疗决策提供了一条有前景的途径。这项研究在预测胶质母细胞瘤复发的时间和位置方面取得了重大进展，可以为治疗决策提供信息以改善患者的治疗结果。© 2024。作者。

Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resonance spectroscopy (WB-MRS) protocol that delves into the intricacies of tumor microenvironments, offering a comprehensive understanding of glioma progression to inform expectant surgical and adjuvant intervention.We investigated five locoregional tumor metabolites in a post-treatment population and applied machine learning (ML) techniques to analyze key relationships within seven regions of interest: contralateral normal-appearing white matter (NAWM), fluid-attenuated inversion recovery (FLAIR), contrast-enhancing tumor at time of WB-MRS (Tumor), areas of future recurrence (AFR), whole-brain healthy (WBH), non-progressive FLAIR (NPF), and progressive FLAIR (PF). Five supervised ML classification models and a neural network were developed, optimized, trained, tested, and validated. Lastly, a web application was developed to host our novel calculator, the Miami Glioma Prediction Map (MGPM), for open-source interaction.Sixteen patients with histopathological confirmation of high-grade glioma prior to WB-MRS were included in this study, totaling 118,922 whole-brain voxels. ML models successfully differentiated normal-appearing white matter from tumor and future progression. Notably, the highest performing ML model predicted glioma progression within fluid-attenuated inversion recovery (FLAIR) signal in the post-treatment setting (mean AUC = 0.86), with Cho/Cr as the most important feature.This study marks a significant milestone as the first of its kind to unveil radiographic occult glioma progression in post-treatment gliomas within 8 months of discovery. These findings underscore the utility of ML-based WB-MRS growth predictions, presenting a promising avenue for the guidance of early treatment decision-making. This research represents a crucial advancement in predicting the timing and location of glioblastoma recurrence, which can inform treatment decisions to improve patient outcomes.© 2024. The Author(s).