胶质母细胞瘤（GBM）是最常见和最具侵袭性的恶性神经胶质瘤，总体中位生存期不到两年。在治疗之前能够预测GBM患者的生存期将有助于改善疾病管理、临床试验入组和患者护理。GBM患者（N=133，平均年龄60.8岁，中位生存期14.1个月，57.9％为男性）是从华盛顿大学医学中心神经外科脑肿瘤服务回顾性招募的。所有患者在手术前完成结构性神经影像和静息态功能磁共振成像（RS-fMRI）。人口统计学信息、皮层厚度（CT）测量和静息态功能网络连接（FC）用于训练一个深度神经网络，以根据生存期（<1年，1-2年，>2年）对患者进行分类。基于训练模型，置换特征重要性确定了最强的生存期预测因子。模型在生存期分类上的交叉验证和留存验证准确率达到了90.6％。最强的人口统计学预测因子是诊断时年龄和性别。生存期的最强CT预测因子包括颞上沟、扣带回、基底节周围、三角旁区域和颞中部位。最强的FC特征主要涉及背侧和下部体感运动、视觉和顶顶顶角叶网络。我们证明了机器学习可以根据多模式神经影像准确地分类GBM患者的生存期，而无需任何手术或医学干预。这些结果在没有涉及症状、治疗、手术后结果或肿瘤基因组信息的情况下实现。我们的结果表明GBMs对大脑的结构和功能组织具有全球影响，并且这一影响与生存有关。© 2023。此为美国政府作品，未在美国受版权保护；可能适用外国版权保护。

Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care.GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models.The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks.We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.