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Histopathological Imaging–Environment Interactions in Cancer Modeling

1
Department of Biostatistics, Yale University, New Haven, CT 06520, USA
2
SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
3
School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China
*
Authors to whom correspondence should be addressed.
Cancers 2019, 11(4), 579; https://doi.org/10.3390/cancers11040579
Received: 26 February 2019 / Revised: 17 April 2019 / Accepted: 19 April 2019 / Published: 24 April 2019
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Abstract

Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological imaging features and clinical/environmental risk factors in cancer modeling. In this article, we explore such a possibility and conduct both marginal and joint interaction analysis. Novel statistical methods, which are “borrowed” from gene–environment interaction analysis, are employed. Analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data is conducted. More specifically, we examine a biomarker of lung function as well as overall survival. Possible interaction effects are identified. Overall, this study can suggest an alternative way of cancer modeling that innovatively combines histopathological imaging and clinical/environmental data. View Full-Text
Keywords: cancer modeling; interaction; histopathological imaging; clinical/environmental factors cancer modeling; interaction; histopathological imaging; clinical/environmental factors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, Y.; Zhong, T.; Wu, M.; Ma, S. Histopathological Imaging–Environment Interactions in Cancer Modeling. Cancers 2019, 11, 579.

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