From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients
Abstract 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 adva...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202408069 |
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| author | Yusheng Guo Tianxiang Li Bingxin Gong Yan Hu Sichen Wang Lian Yang Chuansheng Zheng |
| author_facet | Yusheng Guo Tianxiang Li Bingxin Gong Yan Hu Sichen Wang Lian Yang Chuansheng Zheng |
| author_sort | Yusheng Guo |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-f71e58fe741f4f92a001fdc766d8a896 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-f71e58fe741f4f92a001fdc766d8a8962025-01-13T15:29:43ZengWileyAdvanced Science2198-38442025-01-01122n/an/a10.1002/advs.202408069From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer PatientsYusheng Guo0Tianxiang Li1Bingxin Gong2Yan Hu3Sichen Wang4Lian Yang5Chuansheng Zheng6Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan 430022 ChinaDepartment of Ultrasound State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical. Sciences Peking Union Medical College Beijing 100730 ChinaDepartment of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan 430022 ChinaResearch Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 ChinaSchool of Life Science and Technology Computational Biology Research Center Harbin Institute of Technology Harbin 150001 ChinaDepartment of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan 430022 ChinaDepartment of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan 430022 ChinaAbstract 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.https://doi.org/10.1002/advs.202408069artificial intelligencecancerimmune microenvironmentmedical imagingmulti‐omics analysisprecision medicine |
| spellingShingle | Yusheng Guo Tianxiang Li Bingxin Gong Yan Hu Sichen Wang Lian Yang Chuansheng Zheng From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients Advanced Science artificial intelligence cancer immune microenvironment medical imaging multi‐omics analysis precision medicine |
| title | From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients |
| title_full | From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients |
| title_fullStr | From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients |
| title_full_unstemmed | From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients |
| title_short | From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients |
| title_sort | from images to genes radiogenomics based on artificial intelligence to achieve non invasive precision medicine in cancer patients |
| topic | artificial intelligence cancer immune microenvironment medical imaging multi‐omics analysis precision medicine |
| url | https://doi.org/10.1002/advs.202408069 |
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