高级别胶质瘤（HGG）是中枢神经系统最常见和致命的恶性胶质瘤。目前的护理标准包括手术切除肿瘤，这可能导致功能和认知缺陷。本研究的目的是开发能够在手术前预测 HGG 患者功能结果的模型，促进改善疾病管理和知情患者护理。对来自华盛顿大学医学中心神经外科脑肿瘤服务的成年 HGG 患者 (N = 102) 进行了回顾性研究被招募了。所有患者在手术前均完成了结构神经影像和静息态功能 MRI 检查。使用人口统计学、静息状态网络连接 (FC) 测量、肿瘤位置和肿瘤体积来训练随机森林分类器，以根据卡诺夫斯基表现状态（KPS < 70，KPS ≥ 70）预测功能结果。该模型实现了嵌套KPS 分类的交叉验证准确率为 94.1%，AUC 为 0.97。该模型确定的最强预测因子包括躯体运动网络、视觉网络、听觉网络和奖励网络之间的 FC。根据位置，肿瘤与背侧注意力、扣带盖和基底神经节网络的关系是 KPS 的强有力的预测因子。年龄也是一个强有力的预测因素。然而，肿瘤体积只是一个中等预测因子。目前的工作证明了机器学习能够在手术前对 HGG 患者的术后功能结果进行准确分类。我们的结果表明，FC 和肿瘤相对于特定网络的位置都可以作为功能结果的可靠预测因子，从而为个体患者量身定制个性化治疗方法。© 2024。作者。

High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care.Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70).The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor.The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.© 2024. The Author(s).