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.
Highlights
It is routine for radiologists to use Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Computed Tomography (PET), and other techniques to generate radiological images, which can inform the size, location, and other “macro” features of tumors [2]
The top ranked main effects are from the Geometry and Texture groups, and the top ranked interactions are from the Geometry group and with sex
As I–E interaction analysis has not been conducted in published cancer modeling studies, it is sensible to first employ well-developed methods, and in the future, methods that are more tailored to imaging data may be developed
Summary
Extensive statistical investigations have been conducted, modeling various cancer outcomes/phenotypes. A long array of measurements from different domains have been used in cancer modeling, including clinical/environmental factors, socioeconomic factors, omics (genetic, genomic, epigenetic, proteomic, etc.) measurements, histopathological imaging features, and others. Imaging has been playing an irreplaceable role in cancer practice and research [1]. Pathologists review the slides of representative sections of tissues to make definitive diagnosis. This procedure generates histopathological (diagnostic) images [3]. Histopathological images have been used as the gold standard for diagnosis. Histopathological imaging features have been used to model other cancer outcomes/phenotypes. In [4], they were used for predicting the prognosis of estrogen receptor-negative breast cancer, and a multivariate
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
