Journal article
Hierarchical cancer heterogeneity analysis based on histopathological imaging features
Biometrics, Vol.78(4), pp.1579-1591
08/22/2021
DOI: 10.1111/biom.13544
PMCID: PMC8995088
PMID: 34390584
Abstract
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.
Details
- Title: Subtitle
- Hierarchical cancer heterogeneity analysis based on histopathological imaging features
- Creators
- Mingyang Ren - Chinese Academy of SciencesQingzhao Zhang - Xiamen UniversitySanguo Zhang - Chinese Academy of SciencesTingyan Zhong - Shanghai Jiao Tong UniversityJian Huang - University of IowaShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- Biometrics, Vol.78(4), pp.1579-1591
- DOI
- 10.1111/biom.13544
- PMID
- 34390584
- PMCID
- PMC8995088
- NLM abbreviation
- Biometrics
- ISSN
- 0006-341X
- eISSN
- 1541-0420
- Publisher
- WILEY
- Number of pages
- 13
- Grant note
- CA241699; CA196530; CA204120 / National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA 510320 / Pazhou Lab 1916251 / National Science Foundation; National Science Foundation (NSF) B13028 / Higher Education Discipline Innovation Project Z190004 / Natural Science Foundation of BeijingMunicipality; Beijing Natural Science Foundation Yale Cancer Center PilotAward 71988101 / Basic Scientific Project 12171454; 11971404; 12026604; 71988101 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Language
- English
- Electronic publication date
- 08/22/2021
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257721302771
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