论文标题
根据活检中细胞分布的相似性进行聚类对象的方法
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies
论文作者
论文摘要
在本文中，我们基于从活检中提取的特征引入了一种新颖且可解释的方法来患有癌症的聚类受试者。与现有方法相反，我们在这里建议使用直方图捕获细胞重新分配中的复杂模式，并根据这些重新分配对受试者进行比较。我们在这里描述了我们的完整工作流程，包括创建数据库，细胞分割和表型，复杂特征的计算，功能之间的距离函数的选择，使用该距离的受试者之间的聚类以及对所获得簇的存活分析。我们在苏木精和曙红（H＆E）染色的受试者的组织数据库上说明了我们的方法，患有I期肺腺癌的受试者，我们的结果与预后估计中的现有知识相匹配，具有很高的信心。
In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies. Contrary to existing approaches, we propose here to capture complex patterns in the repartitions of their cells using histograms, and compare subjects on the basis of these repartitions. We describe here our complete workflow, including creation of the database, cells segmentation and phenotyping, computation of complex features, choice of a distance function between features, clustering between subjects using that distance, and survival analysis of obtained clusters. We illustrate our approach on a database of hematoxylin and eosin (H&E)-stained tissues of subjects suffering from Stage I lung adenocarcinoma, where our results match existing knowledge in prognosis estimation with high confidence.
