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

TL;DR

This study compares the effectiveness of histopathological imaging features and gene expression data in modeling lung cancer prognosis, revealing that gene expressions slightly outperform imaging features.

Contribution

It provides a comparative analysis of imaging versus molecular data for lung cancer prognosis using high-dimensional regularization methods.

Findings

Gene expressions have slightly better prognostic performance.

Most gene expressions are weakly correlated with imaging features.

The study offers insights into combining data types for prognosis modeling.

Abstract

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.