Hierarchical Cancer Heterogeneity Analysis Based On Histopathological Imaging Features

时间:2022-09-16         阅读:


主题:Hierarchical Cancer Heterogeneity Analysis Based On Histopathological Imaging Features

讲人:耶鲁大学 Shuangge Ma教授

主持人:统计学院 林华珍教授

时间:9月21日 9:30-10:30

直播平台及会议ID:腾讯会议,会议ID:426 359 881

主办单位:统计研究中心 统计学院 科研处


Dr. Shuangge Ma obtained Ph.D. in Statistics from University of Wisconsin, Madison. He was a Postdoctoral Associate at University of Washington between 2004 and 2006. He is now Professor of Biostatistics at Yale University. His research interests include high-dimensional data analysis, cancer biostatistics, health economics, and others.

Shuangge Ma从威斯康星大学麦迪逊分校获得统计学博士学位。2004年至2006年,他在华盛顿大学做博士后。他现在是耶鲁大学生物统计学教授。他的研究兴趣包括高维数据分析、癌症生物统计学、健康经济学等。


In cancer research, supervised heterogeneity analysis has important implications. Such analysis has been “traditionally” based on clinical/demographic/molecular variables. Recently, histopathological imaging features, which are a “byproduct” of biopsy, have been shown as effective for modeling cancer outcomes, and a handful of supervised heterogeneity analysis has been conducted based on such features. There are two types of histopathological imaging features, which are extracted based on specific biological knowledge and using automated imaging processing software, respectively. In this study, using both types of histopathological imaging features, our goal is to conduct thefirst supervised cancer heterogeneity analysis that has a hierarchical structure. That is, thefirst type of imaging features defines a “rough” structure, and the second type defines a nested and more refined structure. This objective can be achieved using either a penalization or Bayesian approach. Simulation shows satisfactory performance of the proposed analysis. In the analysis of lung adenocarcinoma data, it identifies a heterogeneity structure significantly different from the alternatives and has satisfactory prediction and stability.