Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling

Abstract

For lung and many other cancers, prognosis is essentially important, and extensive modeling has been carried out. Cancer is a genetic disease. In the past 2 decades, diverse molecular data (such as gene expressions and DNA mutations) have been analyzed in prognosis modeling. More recently, histopathological imaging data, which is a "byproduct" of biopsy, has been suggested as informative for prognosis. In this article, with the TCGA LUAD and LUSC data, we examine and directly compare modeling lung cancer overall survival using gene expressions versus histopathological imaging features. High-dimensional penalization methods are adopted for estimation and variable selection. Our findings include that gene expressions have slightly better prognostic performance, and that most of the gene expressions are weakly correlated imaging features. This study may provide additional insight into utilizing the two types of important data in cancer prognosis modeling and into lung cancer overall survival.

Figure 1

Pipeline for extracting imaging features.

Figure 2

Heat map of modeling imaging features using gene expressions. Upper panel: LUAD; lower panel: LUSC.

Figure 3

Analysis of predicting imaging features using gene expressions: mean and standard deviation plots of correlation coefficients from 100 random splits. Left: LUAD. Right: LUSC.