Recent advances in data-driven fusion of multi-modal imaging and genomics for precision medicine

Shuo Wang, Meng Liu, Yan Li, Xinyu Zhang, Mengting Sun, Zian Wang, Ruokun Li, Qirong Li, Qing Li, Yili He, Xumei Hu, Longyu Sun, Fuhua Yan, Mengyao Yu†, Weiping Ding†, Chengyan Wang†

Information Fusion (IF=14.7)

Abstract

Imaging genomics is poised to revolutionize clinical practice by providing deep insights into the genetic underpinnings of disease, enabling early detection, and facilitating personalized treatment strategies. The field has seen remarkable advancements, with significant momentum fueled by cutting-edge imaging techniques, sophisticated data-driven fusion methods, and extensive large cohort datasets. Originally centered on the brain, imaging genomics has now expanded to encompass other organs throughout the body. Due to the highly interdisciplinary nature involving medical imaging, genetics, machine learning, and clinical medicine, readers who wish to conduct research in this field urgently need a comprehensive review. This survey provides an overview of recent advancements in data-driven fusion of multi-modal imaging and genomics, covering applications in the brain, heart, lungs, breasts, abdomen, and bones. We summarize three primary fusion strategies: correlation analysis, causal analysis, and machine learning, discussing their respective application scenarios. Additionally, we explore clinical applications that integrate imaging datasets and genomic data across six major organ systems, and present available open datasets featuring both modalities. Finally, we summarize the challenges and future directions in imaging genomics, which include improving data representation, integrating other omics data, conducting cross-dataset analyses, advancing machine learning algorithms, and investigating organ interactions. This survey aims to review the latest developments in data-driven fusion for precision medicine while providing insights into the future of this evolving field.