Radiogenomics: a Key Component of Precision Cancer Medicine

Overview

Journal Br J Cancer

Specialty Oncology

Date 2023 Jul 6

PMID 37414827

Authors

Zaoqu Liu

Tian Duan

Yuyuan Zhang

Siyuan Weng

Hui Xu

Yuqing Ren

Zhenyu Zhang

Xinwei Han

Affiliations

Soon will be listed here.

Abstract

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.

Citing Articles

A review of AI-based radiogenomics in neurodegenerative disease.

Liu H, Zhang X, Liu Q Front Big Data. 2025; 8:1515341.

PMID: 40052173 PMC: 11882605. DOI: 10.3389/fdata.2025.1515341.

MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network for mutation status prediction.

Yang W, Yoshida S, Zhao J, Wu W, Qiang Y Quant Imaging Med Surg. 2025; 15(2):1190-1211.

PMID: 39995744 PMC: 11847186. DOI: 10.21037/qims-24-1370.

Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base.

Parry T, Gilmore L, Khamoui A Funct Integr Genomics. 2025; 25(1):14.

PMID: 39812750 DOI: 10.1007/s10142-024-01524-7.

A novel MRI-based radiomics for preoperative prediction of lymphovascular invasion in rectal cancer.

Ning X, Yang D, Ao W, Guo Y, Ding L, Zhang Z Abdom Radiol (NY). 2025; .

PMID: 39799548 DOI: 10.1007/s00261-025-04800-7.

Prioritizing Context-Dependent Cancer Gene Signatures in Networks.

Capobianco E, Lisse T, Rieger S Cancers (Basel). 2025; 17(1.

PMID: 39796763 PMC: 11720092. DOI: 10.3390/cancers17010136.

References

Kang J, Rancati T, Lee S, Oh J, Kerns S, Scott J . Machine Learning and Radiogenomics: Lessons Learned and Future Directions. Front Oncol. 2018; 8:228. PMC: 6021505. DOI: 10.3389/fonc.2018.00228. View

Mazurowski M . Radiogenomics: what it is and why it is important. J Am Coll Radiol. 2015; 12(8):862-6. DOI: 10.1016/j.jacr.2015.04.019. View

Garcia Vicente A, Perez-Beteta J, Amo-Salas M, Pena Pardo F, Villena Martin M, Sandoval Valencia H . 18F-Fluorocholine PET/CT in the Prediction of Molecular Subtypes and Prognosis for Gliomas. Clin Nucl Med. 2019; 44(10):e548-e558. DOI: 10.1097/RLU.0000000000002715. View

Boettger L, Salem R, Handsaker R, Peloso G, Kathiresan S, Hirschhorn J . Recurring exon deletions in the HP (haptoglobin) gene contribute to lower blood cholesterol levels. Nat Genet. 2016; 48(4):359-66. PMC: 4811681. DOI: 10.1038/ng.3510. View

Li W, Zhang X, Lu X, You L, Song Y, Luo Z . 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Res. 2017; 27(10):1243-1257. PMC: 5630683. DOI: 10.1038/cr.2017.121. View

Li Z, Bai H, Sun Q, Li Q, Liu L, Zou Y . Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study. Eur Radiol. 2018; 28(9):3640-3650. DOI: 10.1007/s00330-017-5302-1. View

Kickingereder P, Bonekamp D, Nowosielski M, Kratz A, Sill M, Burth S . Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. Radiology. 2016; 281(3):907-918. DOI: 10.1148/radiol.2016161382. View

Saxena S, Jena B, Gupta N, Das S, Sarmah D, Bhattacharya P . Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel). 2022; 14(12). PMC: 9220825. DOI: 10.3390/cancers14122860. View

Shao L, Zuo X, Yang Y, Zhang Y, Yang N, Shen B . The inherited variations of a p53-responsive enhancer in 13q12.12 confer lung cancer risk by attenuating TNFRSF19 expression. Genome Biol. 2019; 20(1):103. PMC: 6533720. DOI: 10.1186/s13059-019-1696-1. View

10.

Gao Y, Wang X, Wang S, Miao Y, Zhu C, Li C . Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram. Front Oncol. 2022; 12:854979. PMC: 9204229. DOI: 10.3389/fonc.2022.854979. View

11.

Brooks S, Brannon A, Parker J, Fisher J, Sen O, Kattan M . ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma. Eur Urol. 2014; 66(1):77-84. PMC: 4058355. DOI: 10.1016/j.eururo.2014.02.035. View

12.

Ramaswamy V, Remke M, Bouffet E, Bailey S, Clifford S, Doz F . Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathol. 2016; 131(6):821-31. PMC: 4867119. DOI: 10.1007/s00401-016-1569-6. View

13.

Kyndi M, Sorensen F, Knudsen H, Overgaard M, Nielsen H, Overgaard J . Estrogen receptor, progesterone receptor, HER-2, and response to postmastectomy radiotherapy in high-risk breast cancer: the Danish Breast Cancer Cooperative Group. J Clin Oncol. 2008; 26(9):1419-26. DOI: 10.1200/JCO.2007.14.5565. View

14.

Xie Y, Liu C, Zhao Y, Gong C, Li Y, Hu S . Heterogeneity derived from F-FDG PET/CT predicts immunotherapy outcome for metastatic triple-negative breast cancer patients. Cancer Med. 2022; 11(9):1948-1955. PMC: 9089221. DOI: 10.1002/cam4.4522. View

15.

Kanas V, Zacharaki E, Thomas G, Zinn P, Megalooikonomou V, Colen R . Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput Methods Programs Biomed. 2017; 140:249-257. DOI: 10.1016/j.cmpb.2016.12.018. View

16.

Berenguer R, Pastor-Juan M, Canales-Vazquez J, Castro-Garcia M, Villas M, Mansilla Legorburo F . Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology. 2018; 288(2):407-415. DOI: 10.1148/radiol.2018172361. View

17.

Liu P, Tan X, Zhang T, Gu Q, Mao X, Li Y . Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics. World J Gastroenterol. 2021; 27(17):2015-2024. PMC: 8108034. DOI: 10.3748/wjg.v27.i17.2015. View

18.

Yan J, Liu L, Wang W, Zhao Y, Li K, Li K . Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma. Front Oncol. 2020; 10:558162. PMC: 7566191. DOI: 10.3389/fonc.2020.558162. View

19.

Kuo M, Gollub J, Sirlin C, Ooi C, Chen X . Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol. 2007; 18(7):821-31. DOI: 10.1016/j.jvir.2007.04.031. View

20.

Nam Y, Park J, Park S, Lee M, Kim M, Nam S . Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system. Eur Radiol. 2021; 31(10):7374-7385. DOI: 10.1007/s00330-021-08015-4. View