Radiogenomics: Current Understandings and Future Perspectives

Abstract

Radiogenomics is a rapidly developing field that links radiological image features (radiomics) to genomic-level data (genomics, transcriptomics, and epigenomics), addressing the limitations of single-omic approaches. Radiomics provides a noninvasive and cost-effective method to capture tissue-level characteristics, while genomics elucidates the underlying molecular mechanisms. The central hypothesis is that the formation of imaging phenotypes is associated with the genetic and molecular processes, and thus can reflect underlying biological activities. This review presents the fundamental principles of radiogenomic analysis, covering key concepts in image analysis and gene analysis, as well as advanced analytical techniques for linking imaging and genomic data. Moreover, we summarize recent research findings across various human diseases, including oncology and nononcology, to highlight the current understandings and achievements in this field. Radiogenomics shows potential in clinical applications for elucidating disease mechanisms, detecting genomic variations noninvasively, and improving prognosis predictions. However, its implementation in clinical practice is limited by data scarcity, analytical methods, and barriers in translational processes. Future research should focus on enhancing data quality and establishing guidelines, developing analytical platforms, and validating current findings through animal models and clinical trials.

Keywords

artificial intelligence / multiomics / precision medicine / radiogenomics

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