Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis.

Autor: Im Y; Department of Biostatistics, Yale University, New Haven, Connecticut, USA., Huang Y; Department of Biostatistics, Yale University, New Haven, Connecticut, USA., Huang J; Department of Statistics and Actuarial Science, University of Iowa, Iowa, Iowa, USA., Ma S; Department of Biostatistics, Yale University, New Haven, Connecticut, USA.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2022 Mar 15; Vol. 41 (6), pp. 1009-1022. Date of Electronic Publication: 2022 Jan 13.
DOI: 10.1002/sim.9309
Abstrakt: Cancer is heterogeneous, and for seemingly similar cancer patients, the associations between an outcome/phenotype and covariates can be different. To describe such differences, finite mixture of regression (FMR) and other modeling techniques have been developed. "Classic" FMR analysis has usually been based on clinical, demographic, and molecular variables. More recently, histopathological imaging data-which is a byproduct of biopsy and enjoys broader data availability and higher cost-effectiveness-has been increasingly used in cancer modeling, although it is noted that its application to cancer FMR analysis still remains limited. In this article, we further advance cancer FMR analysis based on histopathological imaging data. Significantly advancing from the existing analyses under heterogeneity and homogeneity, our goal is to simultaneously use two types of histopathological imaging features, which are extracted based on domain-specific biomedical knowledge and using automated signal processing software, respectively. A significant modeling/methodological advancement is that, to reflect the "increased resolution" of the second type of imaging features over the first type, we impose a hierarchy in the mixture structures. An effective and flexible Bayesian approach is proposed. Simulation shows its competitiveness over several alternatives. The TCGA lung cancer data is analyzed, and interesting heterogeneous structures different from using the alternatives are found. Overall, this study provides a new venue for FMR analysis for cancer and other complex diseases.
(© 2022 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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