Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients.
Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients.
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静息状态功能连接的机器学习分析可预测多形性胶质母细胞瘤患者的生存结局
DOI：
10.3389/fneur.2021.642241
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发表时间：
2021
影响因子：
3.4
通讯作者：
Leuthardt EC
中科院分区：
文献类型：
--
作者：
Lamichhane B;Daniel AGS;Lee JJ;Marcus DS;Shimony JS;Leuthardt EC
多形性胶质母细胞瘤（GBM）是最常见的脑恶性肿瘤。由于目前可用的治疗方法预后较差，因此迫切需要易于获得的非侵入性技术来帮助制定治疗前计划，患者咨询并改善结果。在这项研究中，我们确定了静息状态功能连接（rsFC）将GBM患者根据报告的中位生存期（14.6个月）分为短期和长期生存组的可行性。我们使用了一个支持向量机，在感兴趣的区域之间使用rsFC作为预测特征。采用一种新的混合特征选择方法，首先利用rsFC和操作系统之间的相关性对特征进行过滤，然后利用已建立的递归特征消除（RFE）方法选择最优特征子集。留一个受试者的交叉验证评估了模型的性能。短期和长期生存的分类准确率为71.9%。敏感性为77.1，特异性为65.5%。受试者工作特征曲线下面积为0.752 （95% CI, 0.62 ~ 0.88）。这些发现表明rsFC的高度特异性特征可能预测GBM的生存。综上所述，本研究的发现支持静息状态fMRI和机器学习分析可以实现GBM的放射组学生物标志物，增强对个体患者的护理和规划。
Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.
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影响因子：
3.7
作者：
Leuthardt, Eric C.;Guzman, Gloria;Benzinger, Tammie L. S.
通讯作者：
Benzinger, Tammie L. S.
DOI：
10.1073/pnas.0900924106
发表时间：
2009-03-17
影响因子：
11.1
作者：
Larson-Prior, Linda J.;Zempel, John M.;Raichle, Marcus E.
通讯作者：
Raichle, Marcus E.
影响因子：
45.3
作者：
Hegi, Monika E.;Liu, Lili;Gilbert, Mark R.
通讯作者：
Gilbert, Mark R.
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
5.7
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
4.1
作者：
Laws, ER;Parney, IF;Chang, S
通讯作者：
Chang, S