一种根据活检样本中细胞分布相似性对受试者进行聚类的方法
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies
摘要：
在本文中，我们介绍了一种新的和可解释的方法来聚类受试者患有癌症，根据从他们的活检提取的特征。与现有的方法相反，我们建议在这里捕捉复杂的模式，在他们的细胞使用直方图的重新分区，并比较这些重新分区的基础上的主题。我们在这里描述了我们完整的工作流程，包括创建数据库，细胞分割和表型，复杂特征的计算，特征之间距离函数的选择，使用该距离在受试者之间进行聚类，以及对获得的聚类进行生存分析。我们说明了我们的方法在患有I期肺腺癌的受试者的苏木精和伊红（H&E）染色组织的数据库上，其中我们的结果与预后估计中的现有知识相匹配，具有很高的置信度。
英文摘要：
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.
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