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.

中文翻译：

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用于精准医疗的多模态成像和基因组学数据驱动融合的最新进展


成像基因组学通过提供对疾病遗传基础的深刻见解、实现早期检测并促进个性化治疗策略，有望彻底改变临床实践。该领域取得了显着的进步，尖端成像技术、复杂的数据驱动融合方法和广泛的大型队列数据集推动了巨大的发展势头。成像基因组学最初以大脑为中心，现在已经扩展到包括全身的其他器官。由于涉及医学成像、遗传学、机器学习和临床医学的高度跨学科性质，希望在该领域进行研究的读者迫切需要全面的综述。本调查概述了多模态成像和基因组学的数据驱动融合的最新进展，涵盖大脑、心脏、肺、乳房、腹部和骨骼的应用。我们总结了三种主要的融合策略：相关性分析、因果分析和机器学习，并讨论了它们各自的应用场景。此外，我们还探索了整合六个主要器官系统的成像数据集和基因组数据的临床应用，并提供了具有这两种模式的可用开放数据集。最后，我们总结了成像基因组学面临的挑战和未来方向，包括改进数据表示、整合其他组学数据、进行跨数据集分析、推进机器学习算法和研究器官相互作用。本调查旨在回顾精准医疗数据驱动融合的最新发展，同时提供对这一不断发展领域的未来的见解。