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Hierarchical cancer heterogeneity analysis based on histopathological imaging features
Biometrics ( IF 1.9 ) Pub Date : 2021-08-14 , DOI: 10.1111/biom.13544
Mingyang Ren 1 , Qingzhao Zhang 2 , Sanguo Zhang 1 , Tingyan Zhong 3 , Jian Huang 4 , Shuangge Ma 5
Affiliation  

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 generated as 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. Using both types of histopathological imaging features, our goal is to conduct the first supervised cancer heterogeneity analysis that satisfies 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. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. It has satisfactory statistical and numerical properties. In the analysis of lung adenocarcinoma data, it identifies a heterogeneity structure significantly different from the alternatives and has satisfactory prediction and stability performance.

中文翻译:

基于组织病理学影像特征的分级癌症异质性分析

在癌症研究中,监督异质性分析具有重要意义。这种分析传统上是基于临床/人口统计学/分子变量。最近,作为活检副产品产生的组织病理学成像特征已被证明对模拟癌症结果有效,并且已经基于这些特征进行了一些监督异质性分析。组织病理学成像特征有两种类型,分别是基于特定的生物学知识和使用自动化成像处理软件提取的。使用这两种类型的组织病理学成像特征,我们的目标是进行第一个满足层次结构的监督癌症异质性分析. 也就是说,第一类成像特征定义了一个粗略的结构,第二类定义了一个嵌套的、更精细的结构。开发了一种惩罚方法,该方法受到惩罚融合和稀疏组惩罚的启发,但与它们有很大不同。它具有令人满意的统计和数值特性。在肺腺癌数据分析中,它识别出与备选方案显着不同的异质性结构,具有令人满意的预测和稳定性能。
更新日期:2021-08-14
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