Beyond radiogenomics: advancing imaging integration and multi-omics horizons in cancer precision medicine

Yuming Chen; Huiqin Li; Silu Chen; Hongsheng Ji; Junyi Xin; Mulong Du

doi:10.1016/j.jgg.2026.07.003

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Beyond radiogenomics: advancing imaging integration and multi-omics horizons in cancer precision medicine

doi: 10.1016/j.jgg.2026.07.003

a. Department of Biostatistics, Jiangsu Key Laboratory of Innovative Cancer Diagnosis & Therapeutics, Center for Global Health, School of Public Health, and Department of Urology, Jiangsu Key Laboratory of Urological Disease Prevention and Treatment, the Second Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 211166, China;
b. Department of Environmental Genomics and Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China;
c. Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China

Funds:

This work was supported by the Natural Science Foundation of Jiangsu Province (BK20230003).

Received Date: 2026-02-23
Accepted Date: 2026-07-07
Rev Recd Date: 2026-06-25

Available Online: 2026-07-14

Abstract

Abstract

Cancer remains the leading cause of death worldwide, presenting substantial challenges to precision medicine due to its complex heterogeneity. Radiogenomics, as a method combining quantitative radiologic data with genomic information, provides a robust analysis framework to assess tumor heterogeneity and cancer progression. Here, we summarize the application of radiogenomics into two key fusion methods: feature-level and decision-level fusion. Feature-level fusion combines multimodal data into a rich feature set to improve the predictive power of models, while decision-level fusion integrates decision results from multiple independent models to improve robustness and reliability. Furthermore, we explore the integration of radiomics with various omics technologies, including transcriptomics, metabolomics, and proteomics. This integration enables a deeper understanding of the dynamic tumor microenvironment, metabolic dysregulation, and cancer progression mechanisms. Finally, we provide a detailed overview of publicly available datasets relevant to radiogenomics research, such as The Cancer Imaging Archive, cBioPortal, UK Biobank and Human Connectome Project; and further describe multiple types of omics data and sample characteristics for each resource for the benefit to readers. In summary, this review charts a path beyond radiogenomics by advancing radiomics and multi-omics horizons to transform precision medicine in cancer.
- Cancer,
- radiomics,
- genomics,
- radiogenomics,
- fusion

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