Augmented cell-graphs for automated cancer diagnosis
Augmented cell-graphs for automated cancer diagnosis
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DOI：
10.1093/bioinformatics/bti1100
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发表时间：
2005-09-01
期刊：
影响因子：
5.8
通讯作者：
Yener, B
中科院分区：
文献类型：
--
作者：
Demir, C;Gultekin, SH;Yener, B
本工作报告了一种新的计算方法的基础上增强细胞图（ACG），这是从低放大率的组织图像构造的脑癌（恶性胶质瘤）的数学诊断。ACG是一个简单的，无向的，加权的和完全的图，其中一个节点表示一个细胞簇和一对节点之间的边定义了它们之间的二元关系。ACG的节点和边缘都被分配权重以捕获关于组织拓扑的更多信息。在这项工作中，实验是在一个数据集上进行的，该数据集由来自60名不同患者的646份人脑活检样本组成。结果表明，ACG方法在胶质瘤诊断的组织水平上的敏感性为97.53%，特异性为93.33%和98.15%（分别为发炎和健康）。
This work reports a novel computational method based on augmented cell-graphs (ACG), which are constructed from low-magnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both the nodes and the edges of an ACG are assigned weights to capture more information about the topology of the tissue. In this work, the experiments are conducted on a dataset that is comprised of 646 human brain biopsy samples from 60 different patients. It is shown that the ACG approach yields sensitivity of 97.53% and specificities of 93.33 and 98.15% (for the inflamed and healthy, respectively) at the tissue level in glioma diagnosis.