March 3, 2026Open Access

A radiogenomic approach in lung adenocarcinoma to unravel molecular mechanisms underlying CT imaging phenotypes

Key Points

Distinct shifts in tumor texture correlate with hypomethylation levels in lung adenocarcinoma, indicating significant biological insights.
Analysis of 86 image features reveals meaningful connections between CT imaging phenotypes and molecular mechanisms examined.
The bioinformatics workflow enhances multi-omics integration, allowing for a deeper understanding of tumor characteristics and responses.
This method suggests broad applicability for future studies across various cancer types and non-malignant diseases.

Abstract

The herein-developed bioinformatics workflow aims to improve the interpretability of results from a multi-omics integration. Extraction of 86 image features from the CT image series describing tumor characteristics is followed by differential expression/methylation analysis. Regulation of signaling and cellular response to organic substance were reflected in CT image phenotypes. Hypomethylation showed a distinct shift in tumor texture. The described approach is generalizable to other cancer entities or non-malignant diseases to broaden the interpretability of future radiogenomics studies.

A radiogenomic approach in lung adenocarcinoma to unravel molecular mechanisms underlying CT imaging phenotypes

Key Points

Abstract

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