耶鲁大学马双鸽教授:Hierarchical Cancer Heterogeneity Analysis Based On Histopathological Imaging Features
主 题：Hierarchical Cancer Heterogeneity Analysis Based On Histopathological Imaging Features
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.
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 the ﬁrst supervised cancer heterogeneity analysis that has a hierarchical structure. That is, the ﬁrst 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.