From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients

作者：Yusheng Guo, Tianxiang Li, Bingxin Gong, Yan Hu, Sichen Wang, Lian Yang, Chuansheng Zheng · 发表于：Advanced Science · 年份：2024 · DOI：10.1002/advs.202408069 · 被引用次数：38 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Cancer Genomics and Diagnostics、Lung Cancer Diagnosis and Treatment

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.