Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging
Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging
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DOI：
10.1002/nbm.3163
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发表时间：
2014-09-01
影响因子：
2.9
通讯作者：
Howe, Franklyn A.
中科院分区：
文献类型：
--
作者：
Yang, Guang;Jones, Timothy L.;Howe, Franklyn A.
高级别多形性胶质母细胞瘤(GBM)和孤立性转移瘤(MET)的处理和治疗有很大的不同，并影响预后和后续的临床结果。在单发MET的情况下，使用传统放射学的诊断可能是模棱两可的。目前，明确的诊断是基于对活检样本的组织病理学分析。在这里，我们提出了一个基于MRI的用于区分GBM和孤立性MET的计算机化决策支持框架，其中包括：(I)基于扩散张量成像的半自动分割方法；(Ii)二维形态特征提取和选择；以及(Iii)用于肿瘤自动分类的模式识别模块。手术前、立体定向活检或手术切除的组织病理学分析提供了基本事实。我们的二维形态分析优于以前的方法，交叉验证准确率高达97.9%，基于神经网络的分类器的接收器工作特征曲线下面积为0.975。版权所有(C)2014 John Wiley&Sons，Ltd.
The management and treatment of high-grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi-automatic segmentation method based on diffusion tensor imaging; (ii) two-dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pretreatment stereotactic biopsy or at surgical resection. Our two-dimensional morphological analysis outperforms previous methods with high cross-validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks-based classifier. Copyright (C) 2014 John Wiley & Sons, Ltd.