  For most if not all cancers, prognosis is of significant importance, and
extensive modeling research has been conducted. With the genetic nature of
cancer, in the past two decades, multiple types of molecular data (such as gene
expressions and DNA mutations) have been explored. More recently,
histopathological imaging data, which is routinely collected in biopsy, has
been shown as informative for modeling prognosis. In this study, using the TCGA
LUAD and LUSC data as a showcase, we examine and compare modeling lung cancer
overall survival using gene expressions versus histopathological imaging
features. High-dimensional regularization methods are adopted for estimation
and selection. Our analysis shows that gene expressions have slightly better
prognostic performance. In addition, most of the gene expressions are found to
be weakly correlated imaging features. It is expected that this study can
provide some insight into utilizing the two types of important data in cancer
prognosis modeling and into lung cancer overall survival.
