Data-Driven Radiogenomic Approach for Deciphering Molecular Mechanisms Underlying Imaging Phenotypes in Lung Adenocarcinoma: A Pilot Study

Abstract

The heterogeneity of lung tumor nodules is reflected in their phenotypic characteristics in radiological images. The radiogenomics field employs quantitative image features combined with transcriptome expression levels to understand tumor heterogeneity molecularly. Due to the different data acquisition techniques for imaging traits and genomic data, establishing meaningful connections poses a challenge. We analyzed 86 image features describing tumor characteristics (such as shape and texture) with the underlying transcriptome and post-transcriptome profiles of 22 lung cancer patients (median age 67.5 years, from 42 to 80 years) to unravel the molecular mechanisms behind tumor phenotypes. As a result, we were able to construct a radiogenomic association map (RAM) linking tumor morphology, shape, texture, and size with gene and miRNA signatures, as well as biological correlates of GO terms and pathways. These indicated possible dependencies between gene and miRNA expression and the evaluated image phenotypes. In particular, the gene ontology processes “regulation of signaling” and “cellular response to organic substance” were shown to be reflected in CT image phenotypes, exhibiting a distinct radiomic signature. Moreover, the gene regulatory networks involving the TFs TAL1, EZH2, and TGFBR2 could reflect how the texture of lung tumors is potentially formed. The combined visualization of transcriptomic and image features suggests that radiogenomic approaches could identify potential image biomarkers for underlying genetic variation, allowing a broader view of the heterogeneity of the tumors. Finally, the proposed methodology could also be adapted to other cancer types to expand our knowledge of the mechanistic interpretability of tumor phenotypes.

Differentiating inflammatory and malignant pulmonary lesions on 3T lung MRI with radiomics of apparent diffusion coefficient maps and T2w derived radiomic feature maps

“…Overcoming the ambiguity of lung nodules reflected by their overlapping imaging appearance has been a concern of many investigators ( 47 ). For example, Yang et al developed a helpful novel statistical model combining radiomics and T2-based quantitative parameters derived from T2-fBLADE-TSE sequences to differentiate between malignant and benign pulmonary nodules based on their findings in 107 pulmonary nodules in 96 patients ( 48 ).…”