Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
复制标题
DOI：
10.1016/j.nicl.2014.08.001
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Miyamoto S
中科院分区：
文献类型：
--
作者：
Inano R;Oishi N;Kunieda T;Arakawa Y;Yamao Y;Shibata S;Kikuchi T;Fukuyama H;Miyamoto S
胶质瘤是最常见的轴内原发性脑肿瘤，因此，预测胶质瘤分级将影响治疗策略。虽然存在基于来自诊断图像的单个或多个参数的几种方法，但是用于术前确定胶质瘤等级的确定性方法仍然未知。我们的目标是开发一种使用术前扩散张量图像的多个参数的无监督方法，以获得可以对神经胶质瘤进行视觉分级的集群图像。14例低级别胶质瘤患者和19例高级别胶质瘤患者在肿瘤切除前接受了弥散张量成像和三维T1加权磁共振成像。从弥散张量成像中提取弥散加权成像、分数各向异性、第一特征值、第二特征值、第三特征值、平均弥散度和原始T2信号（无弥散加权）等7个特征作为多个参数。我们开发了一种两级聚类方法，用于自组织映射，然后使用K-means算法对大量具有全脑七个特征的输入向量进行无监督聚类。利用自组织映射将矢量进行分组，得到原始聚类，再利用K均值聚类算法将原始聚类划分为数量较少的类，得到基于体素的扩散张量聚类图像。此外，我们还确定了基于扩散张量的聚类图像是否真的有助于以监督的方式预测术前胶质瘤级别。根据在扩散张量成像空间上手动追踪的感兴趣区域计算基于扩散张量的聚类图像中的每个类别的比率，并计算共同的对数比率尺度。然后，我们应用支持向量机作为区分低级别和高级别胶质瘤的分类器。因此，灵敏度，特异性，准确性和曲线下面积的16类扩散张量为基础的聚类图像，表现出最好的性能区分高，低级别胶质瘤的受试者工作特征曲线分别为0.848，0.745，0.804和0.912。此外，16类基于扩散张量的聚类图像的每个类的对数比率值在低级别和高级别胶质瘤之间进行比较，并且高级别胶质瘤中的14、15和16类的对数比率值显著高于低级别胶质瘤中的那些（分别为p < 0.005、p < 0.001和p < 0.001）。这些类别包括七个基于扩散张量成像的参数的不同模式。结果表明，基于体素的扩散张量聚类图像的基于多重扩散张量成像的参数可以帮助区分低级别和高级别胶质瘤。我们已经开发了一种新的无监督的方法，基于体素的聚类成像。聚类图像中的每个类别比率区分高级别和低级别胶质瘤。16类聚类图像的分化表现出最好的性能。每个类包括七个基于扩散张量的特征的不同模式。扩散张量图像的多个参数对胶质瘤分级是有用的。
Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic curves from the 16-class diffusion tensor-based clustered images that showed the best performance for differentiating high- and low-grade gliomas were 0.848, 0.745, 0.804 and 0.912, respectively. Furthermore, the log-ratio value of each class of the 16-class diffusion tensor-based clustered images was compared between low- and high-grade gliomas, and the log-ratio values of classes 14, 15 and 16 in the high-grade gliomas were significantly higher than those in the low-grade gliomas (p < 0.005, p < 0.001 and p < 0.001, respectively). These classes comprised different patterns of the seven diffusion tensor imaging-based parameters. The results suggest that the multiple diffusion tensor imaging-based parameters from the voxel-based diffusion tensor-based clustered images can help differentiate between low- and high-grade gliomas. We have developed a novel unsupervised method for voxel-based clustered imaging. Each class ratio in clustered images differentiated high from low-grade gliomas. The 16-class clustered images showed the best performance for the differentiation. Each class comprised different patterns of the seven diffusion tensor-based features. Multiple parameters from diffusion tensor images are useful for glioma grading.
登录
查看更多内容
影响因子：
5.7
作者：
Klawiter, Eric C.;Schmidt, Robert E.;Trinkaus, Kathryn;Liang, Hsiao-Fang;Budde, Matthew D.;Naismith, Robert T.;Song, Sheng-Kwei;Cross, Anne H.;Benzinger, Tammie L.
通讯作者：
Benzinger, Tammie L.
影响因子：
3.9
作者：
Kallenberg, K.;Goldmann, T.;Menke, J.;Strik, H.;Bock, H. C.;Stockhammer, F.;Buhk, J. H.;Frahm, J.;Dechent, P.;Knauth, M.
通讯作者：
Knauth, M.
影响因子：
1.7
作者：
Klingberg, T;Vaidya, CJ;Hedehus, M
通讯作者：
Hedehus, M
影响因子：
3.3
作者：
JEZZARD, P;BALABAN, RS
通讯作者：
BALABAN, RS
影响因子：
1.9
作者：
Inoue, T;Ogasawara, K;Kabasawa, H
通讯作者：
Kabasawa, H