MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas.
MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas.
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DOI：
10.1118/1.4948668
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发表时间：
2016-06
期刊：
影响因子：
3.8
通讯作者：
Erickson BJ
中科院分区：
文献类型：
--
作者：
Korfiatis P;Kline TL;Coufalova L;Lachance DH;Parney IF;Carter RE;Buckner JC;Erickson BJ
影像学生物标志物研究的重点是发现放射学特征和组织学发现之间的关系。在胶质母细胞瘤患者中，O6-甲基鸟嘌呤甲基转移酶（MGMT）基因启动子的甲基化与当前标准治疗的有效性增加呈正相关。在本文中，作者研究了纹理特征作为潜在的成像生物标志物，用于捕获多形性胶质母细胞瘤（GBM）肿瘤的MGMT甲基化状态时，结合监督分类方案。对155例已知MGMT甲基化状态的GBM患者进行了回顾性研究。共现和运行长度纹理特征计算，支持向量机（SVM）和随机森林分类器被用来预测MGMT甲基化状态。最佳分类系统（基于SVM的分类器）的受试者工作特征（ROC）曲线下的最大面积为0.85（95%置信区间：0.78 - 0.91）使用四个纹理特征（相关性、能量、熵和局部强度），产生ROC曲线的最佳阈值，灵敏度为0.803，特异性为0.813。结果表明，MRI纹理特征的监督机器学习可以预测术前GBM肿瘤中MGMT甲基化状态，从而提供了一种新的非侵入性成像生物标志物。
Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.