Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data

Yang Luo; Yilin Li; Mengjie Fang; Shuo Wang; Lizhi Shao; Ruiyang Zou; Di Dong; Zhenyu Liu; Jingwei Wei; Jie Tian

doi:10.1016/j.intonc.2024.10.003

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Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data

Review article | 更新时间：2026-06-03

- Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data
- Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data
- 智能肿瘤学 2025年1卷第1期页码：17-30
- 作者机构：
  
  1. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences,Beijing,China,100190
  2. Beijing Key Laboratory of Molecular Imaging,Beijing,China,100190
  3. School of Life Science and Technology, Xidian University,Shaanxi,Xi’an,China,710126
  4. School of Public Health, Anhui Medical University,Anhui,Hefei,China,230032
  5. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University,Beijing,China,100191
  6. School of Engineering Medicine, Beihang University,Beijing,China,100191
  7. Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University,Beijing,China,100191
  8. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University,Shaanxi,Xi’an,China,710126
- 作者简介：
- 基金信息：
- DOI：10.1016/j.intonc.2024.10.003
  中图分类号：
- 收稿：2024-07-19，
  
  网络首发：2024-11-22，
  
  纸质出版：2025
- 稿件说明：
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Yang Luo, Yilin Li, Mengjie Fang, 等. Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data[J]. 智能肿瘤学, 2025,1(1):17-30.

Yang Luo, Yilin Li, Mengjie Fang, et al. Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data[J]. Intelligent Oncology, 2025, 1(1): 17-30.
Yang Luo, Yilin Li, Mengjie Fang, 等. Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data[J]. 智能肿瘤学, 2025,1(1):17-30. DOI： 10.1016/j.intonc.2024.10.003.

Yang Luo, Yilin Li, Mengjie Fang, et al. Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data[J]. Intelligent Oncology, 2025, 1(1): 17-30. DOI： 10.1016/j.intonc.2024.10.003.

摘要

The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological data. This review aims to explore the synergy between three key omics domains: radiomics

genoproteomics

and pathomics. Radiomics involves extracting high-dimensional data from medical images

providing valuable insights into tumor heterogeneity and treatment response. Genoproteomics

encompassing both genomic and proteomic analyses

delves into the molecular mechanisms driving cancer progression and therapeutic resistance. Pathomics leverages advanced digital pathology techniques to quantitatively analyze tissue architecture and cellular morphology. We provide an in-depth overview of the methodologies and tools employed in each omics field

highlighting their specific applications in oncology

including cancer diagnosis

biomarker discovery

and prediction of treatment outcomes. Furthermore

we discuss the integration of multi-omics data

addressing the challenges and innovative solutions for harmonizing these complex datasets. Through an examination of recent advancements and case studies

we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.

Abstract

genoproteomics

and pathomics. Radiomics involves extracting high-dimensional data from medical images

providing valuable insights into tumor heterogeneity and treatment response. Genoproteomics

encompassing both genomic and proteomic analyses

highlighting their specific applications in oncology

including cancer diagnosis

biomarker discovery

and prediction of treatment outcomes. Furthermore

we discuss the integration of multi-omics data

addressing the challenges and innovative solutions for harmonizing these complex datasets. Through an examination of recent advancements and case studies

we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.

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