ArticleScientific reports2020
Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
- GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.Bioinformatics (Oxford, England) · 2025Article
- Article
- Article
- Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis.Statistics in medicine · 2022Article
- Computed tomography radiomic features hold prognostic utility for canine lung tumors: An analytical study.PloS one · 2021Article
- Clinical Data Analysis Identifies Prognostic Long Non-coding RNA Signatures in Lung Adenocarcinoma.Cancer genomics & proteomicsArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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