- 1. Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, P.R. China;
- 2. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, P.R. China;
LIU Zaiyi,
Email: liuzaiyi@gdph.org.cn
Cancer presents a significant global public health challenge, impacting human health on a broad scale. In recent years, the rapid advancement of big data-based bioinformatics has unveiled crucial potential in precision oncology through various omics research methods. The advent of radiomics has notably expanded the application scope of medical imaging in the field. However, due to the multi-level and multifactorial nature of tumor initiation and progression, a single omics information remains insufficient to meet the demands for advancing precision oncology strategies. Multi-omics research has become an emerging trend. The research paradigm integrating radiomics with other omics offers a novel perspective for personalized decision-making in oncology. Nevertheless, there persists a need to introduce more integrated new technologies and theories to expedite the progress of this field.
Citation： HUANG Yanqi, LIU Zaiyi. Integrating radiomics into multi-omics research: unveiling new perspectives on precision oncology. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2024, 31(3): 257-264. doi: 10.7507/1007-9424.202312050 Copy
Copyright © the editorial department of CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY of West China Medical Publisher. All rights reserved
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- 45. Wang X, Xie T, Luo J, et al. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment. Breast Cancer Res, 2022, 24(1): 20.
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- 52. Cheerla A, Gevaert O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics, 2019, 35(14): i446-i454.
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