- 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
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- 7. Conti A, Duggento A, Indovina I, et al. Radiomics in breast cancer classification and prediction. Semin Cancer Biol, 2021, 72: 238-250.
- 8. Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
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- 10. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 2009, 10(1): 57-63.
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- 19. Yang J, Chen Y, Jing Y, et al. Advancing CAR T cell therapy through the use of multidimensional omics data. Nat Rev Clin Oncol, 2023, 20(4): 211-228.
- 20. Gao Q, Zhu H, Dong L, et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell, 2019, 179(2): 561-577.
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- 34. Fan L, Cao Q, Ding X, et al. Radiotranscriptomics signature-based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels. Cancer Med, 2020, 9(14): 5065-5074.
- 35. Le NQK, Hung TNK, Do DT, et al. Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Comput Biol Med, 2021, 132: 104320.
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- 43. Steyaert S, Pizurica M, Nagaraj D, et al. Multimodal data fusion for cancer biomarker discovery with deep learning. Nat Mach Intell, 2023, 5(4): 351-362.
- 44. Vaidya P, Bera K, Gupta A, et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in StageⅠ, Ⅱ resectable non-small cell lung cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health, 2020, 2(3): e116-e128.
- 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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- 47. Boehm KM, Aherne EA, Ellenson L, et al. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer, 2022, 3(6): 723-733.
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- 52. Cheerla A, Gevaert O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics, 2019, 35(14): i446-i454.
- 53. Ning Z, Du D, Tu C, et al. Relation-aware shared representation learning for cancer prognosis analysis with auxiliary clinical variables and incomplete multi-modality data. IEEE Trans Med Imaging, 2022, 41(1): 186-198.
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