Share this content in WeChat
Research advances in radiogenomics
JIA Yushan  WU Hui 

Cite this article as: Jia YS, Wu H. Research advances in radiogenomics[J]. Chin J Magn Reson Imaging, 2022, 13(3): 166-170. DOI:10.12015/issn.1674-8034.2022.03.040.

[Abstract] Radiogenomics provides non-invasive, real-time and continuous monitoring of gene expression by establishing a link between genes and non-invasive imaging features, thus enabling diagnosis, grading, treatment and prognosis of diseases through imaging examinations to be predicted and analyzed at the molecular level to achieve precision medicine. In recent years, more and more scholars have started to pay attention to radiogenomics, and have carried out research in different fields and made some progress. This review describes the recent advances and potential of radiogenomics in the diagnosis and prognosis prediction of glioma, lung cancer, breast cancer, colorectal cancer,kidney cancer and prostate cancers.
[Keywords] radiogenomics;glioma;lung cancer;breast cancer;colorectal cancer;kidney cancer;prostate cancer

JIA Yushan   WU Hui*  

Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010050, China

Wu H, E-mail:

Conflicts of interest   None.

Received  2021-11-12
Accepted  2022-03-01
DOI: 10.12015/issn.1674-8034.2022.03.040
Cite this article as: Jia YS, Wu H. Research advances in radiogenomics[J]. Chin J Magn Reson Imaging, 2022, 13(3): 166-170.DOI:10.12015/issn.1674-8034.2022.03.040

Lee G, Lee HY, Ko ES, et al. Radiomics and imaging genomics in precision medicine[J]. Glioma Imaging, 2017, 1(1): 10-31. DOI: 10.23838/pfm.2017.00101.
Bodalal Z, Trebeschi S, Nguyen-Kim TDL et al. Radiogenomics: bridging imaging and genomics[J]. Abdom Radiol (NY), 2019, 44: 1960-1984. DOI: 10.1007/s00261-019-02028-w.
Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges[J]. J Med Imaging (Bellingham), 2021, 8(3): 031902. DOI: 10.1117/1.JMI.8.3.031902.
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary[J]. Acta neuropathologica, 2016, 131(6): 803-820. DOI: 10.1007/s00401-016-1545-1.
Kickingereder P, Bonekamp D, Nowosielski M, et al. Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features[J]. Radiology, 2016, 281: 907-918. DOI: 10.1148/radiol.2016161382.
Xi YB, Guo F, Xu ZL, et al. Radiomics signature: a potential biomarker for the prediction of MGMT promoter methylation in glioblastoma[J]. J Magn Reson Imaging, 2018, 47(5): 1380-1387. DOI: 10.1002/jmri.25860.
Korfiatis P, Kline TL, Coufalova L, et al. MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas[J]. Med Phys, 2016, 43(6): 2835-2844. DOI: 10.1118/1.4948668.
Akbari H, Bakas S, Pisapia JM, et al. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature[J]. Neuro Oncol, 2018, 20(8): 1068-1079. DOI: 10.1093/neuonc/noy033.
Hu, LS, Wang L, Hawkins-Daarud A, et al. Uncertainty quantifification in the radiogenomics modeling of EGFR amplifification in glioblastoma[J]. Sci Rep, 2021, 11(1): 3932. DOI: 10.1038/s41598-021-83141-z.
Qi S, Yu L, Li H, et al. Isocitrate dehydrogenase mutation is associated with tumor location and magnetic resonance imaging characteristics in astrocytic neoplasms[J]. Oncol Lett, 2014, 7(6): 1895-1902. DOI: 10.3892/ol.2014.2013.
Zhang B, Chang K, Ramkissoon S, et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas[J]. Neuro Oncol2017, 19(1): 109-117. DOI: 10.1093/neuonc/now121.
Kanas VG, Zacharaki EI, Thomas GA, et al. Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma[J]. Computer Methods and Programs in Biomedicine, 2017, 140: 249-257. DOI: 10.1016/j.cmpb.2016.12.018.
Han Y, Xie Z, Zang Y, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas[J]. J Neurooncol, 2018, 140(2): 297-306. DOI: 10.1007/s11060-018-2953-y.
Zhou H, Chang K, Bai HX, et al. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas[J]. J Neurooncol, 2019, 142(2): 299-307. DOI: 10.1007/s11060-019-03096-0.
Gevaert O, Mitchell LA, Achrol AS, et al. Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features[J]. Radiology, 2015, 276(1): 313. DOI: 10.1148/radiol.2015154019.
Rizzo S, Petrella F, Buscarino V, et al. CT Radiogenomic Characterization of EGFR, K-RAS, and ALK Mutations in Non-Small Cell Lung Cancer[J]. European Radiology, 2016, 26(1): 32-42. DOI: 10.1007/s00330-015-3814-0.
Sacconi B, Anzidei M, Leonardi A, et al. Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: A correlation with EGFR mutations and survival rates[J]. Clinical Radiology, 2017, 72(6): 443-450. DOI: 10.1016/j.crad.2017.01.015.
Zhang L, Chen B, Liu X, et al. Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer[J]. Translational Oncology, 2018, 11(1): 94-101. DOI: 10.1016/j.tranon.2017.10.012.
Ninomiya K, Arimura H, Chan WY, et al. Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers. PLoS One, 2021, 16(1): e0244354. DOI: 10.1371/journal.pone.0244354.
Nair JKR, Saeed UA, McDougall CC, et al. Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer[J]. Can Assoc Radiol J, 2021, 72(1): 109-119. DOI: 10.1177/0846537119899526.
Mendoza DP, Stowell J, Muzikansky A, et al. Computed Tomography Imaging Characteristics of Non-Small-Cell Lung Cancer With Anaplastic Lymphoma Kinase Rearrangements: A Systematic Review and Meta-Analysis[J]. Clinical Lung Cancer, 2019, 20(5): 339-349. DOI: 10.1016/j.cllc.2019.05.006.
Park J, Kobayashi Y, Urayama KY, et al. Imaging Characteristics of Driver Mutations in EGFR, KRAS, and ALK among Treatment-Naïve Patients with Advanced Lung Adenocarcinoma[J]. PLoS One, 2016, 11(8): e0161081. DOI: 10.1371/journal.pone.0161081.
Bitencourt A, Daimiel Naranjo I, Lo Gullo R, et al. AI-enhanced breast imaging: Where are we and where are we heading?[J]. Eur J Radiol, 2021, 142: 109882. DOI: 10.1016/j.ejrad.2021.109882.
Mazurowski MA, Zhang J, Grimm LJ, et al. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging[J]. Radiology, 2014, 273(2): 365-372. DOI: 10.1148/radiol.14132641.
Dilorenzo G, Telegrafo M, La Forgia D, et al. Breast MRI background parenchymal enhancement as an imaging bridge to molecular cancer sub-type[J]. Eur J Radiol, 2019, 113: 148-152. DOI: 10.1016/j.ejrad.2019.02.018.
Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion weighted imaging and breast cancer biomarkers[J]. Eur Radiol, 2012, 22(7): 1519-1528. DOI: 10.1007/s00330-012-2403-8.
Kim EJ, Kim SH, Park GE, et al. Histogram analysis of apparent diffusion coeffificient at 3.0t: correlation with prognostic factors and subtypes of invasive ductal carcinoma[J]. J Magn Reson Imaging, 2015, 42 (6): 1666-1678. DOI: 10.1002/jmri.24934.
Park SH, Choi HY, Hahn SY. Correlations between apparent diffusion coeffificient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla[J]. J Magn Reson Imaging, 2015, 41(1): 175-182. DOI: 10.1002/jmri.24519.
Wang Jeff, Kato Fumi, Oyama-Manabe Noriko, et al. Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study[J]. PLOS One, 2015, 10(11): e0143308. DOI: 10.1371/journal.pone.0143308.
Thakur SB, Durando M, Milans S, et al. Apparent diffusion coefficient in estrogen receptor-positive and lymph node-negative invasive breast cancers at 3.0T DW-MRI: a potential predictor for an Oncotype Dx test recurrence score[J]. J Magn Reson Imaging, 2018, 47(2): 401-409. DOI: 10.1002/jmri.25796.
Li H, Zhu Y, Burnside ES, et al. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays[J]. Radiology, 2016, 281(2): 382-391. DOI: 10.1148/radiol.2016152110.
Therkildsen C, Bergmann TK, Henrichsen-Schnack T, et al. The predictive value of KRAS, NRAS, BRAF, PIK3CA and PTEN for anti-EGFR treatment in metastatic colorectal cancer: A systematic review and meta-analysis[J]. Acta Oncol, 2014, 53(7): 852-864. DOI: 10.3109/0284186X.2014.895036.
Shin YR, Kim KA, Im S, et al. Prediction of KRAS Mutation in Rectal Cancer Using MRI[J]. Anticancer Res, 2016, 36(9): 4799-4804. DOI: 10.21873/anticanres.11039.
Lubner M, Stabo N, Lubner S, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes[J]. Abdom imaging, 2015, 40(7): 2331-2337. DOI: 10.1007/s00261-015-0438-4.
Yang L, Dong D, Fang M, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?[J]. Eur Radiol, 2018, 28(5): 2058-2067. DOI: 10.1007/s00330-017-5146-8.
Badic B, Hatt M, Durand S, et al. Radiogenomics-based cancer prognosis in colorectal cancer[J]. Sci Rep, 2019, 9(1): 9743. DOI: 10.1038/s41598-019-46286-6.
Incoronato M, Aiello M, Infante T, et al. Radiogenomic Analysis of Oncological Data: A Technical Survey[J]. Int J Mol Sci, 2017, 18(4): 805. DOI: 10.3390/ijms18040805.
Ge Y, Xu L, Zhou C, et al. BAP1A Mutation-specific MicroRNA Signature Predicts Clinical Outcomes in Clear Cell Renal Cell Carcinoma Patients with Wild-type[J]. J Cancer, 2017,8(13): 2643-2652. DOI: 10.7150/jca.20234.
Shinagare AB, Vikram R, Jaffe C, et al. Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group[J]. Abdom Imaging, 2015, 40(6): 1684-1692. DOI: 10.1007/s00261-015-0386-z.
Udayakumar D,Zhang Z, Xi Y, et al. Deciphering Intratumoral Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma with a Radiogenomics Platform[J]. Clin Cancer Res, 2021, 27(17): 4794-4806. DOI: 10.1158/1078-0432.CCR-21-0706
Lee HW, Cho HH, Joung JG, et al. Integrative Radiogenomics Approach for Risk Assessment of Post-Operative Metastasis in Pathological T1 Renal Cell Carcinoma: A Pilot Retrospective Cohort Study[J]. Cancers (Basel), 2020, 12(4): 866. DOI: 10.3390/cancers12040866.
Jamshidi N, Jonasch E, Zapala M, et al. The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma[J]. Radiology, 2015, 277(1): 114-123. DOI: 10.1148/radiol.2015150800.
Jamshidi N, Jonasch E, Zapala M, et al. The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial[J]. Eur Radiol, 2015, 26(8): 2798-2807. DOI: 10.1007/s00330-015-4082-8.
Galletti G, Leach BI, Lam L, et al. Mechanisms of resistance to systemic therapy in metastatic castration-resistant prostate cancer. Cancer treatment reviews[J]. Cancer Treat Rev, 2017, 57: 16-27. DOI: 10.1016/j.ctrv.2017.04.008.
McCann SM, Jiang Y, Fan X, et al. Quantitative multiparametric MRI features and PTEN expression of peripheral zone prostate cancer: a pilot study[J]. Am J Roentgenol, 2016, 206(3): 559-565. DOI: 10.2214/AJR.15.14967.
Bates A, Miles K. Prostate -specific membrane antigen PET/MRI validation of MR textural analysis for detection of transition zone prostate cancer[J]. Eur Radiol, 2017, 27(12): 5290-5298. DOI: 10.1007/s00330-017-4877-x.
Renard-Penna R, Cancel-Tassin G, Comperat E, et al. Multiparametric magnetic resonance imaging predicts postoperative pathology but misses aggressive prostate cancers as assessed by cell cycle progression score[J]. J Urol, 2015, 194(6): 1617-1623. DOI: 10.1016/j.juro.2015.06.107.
Wibmer AG, Robertson NL, Hricak H, et al. Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer[J]. Abdom Radiol (NY), 2019, 44(8): 2864-2873. DOI: 10.1007/s00261-019-02023-1.
Stoyanova R, Pollack A, Takhar M, et al. Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies[J]. Oncotarget, 2016, 7(33): 53362-53376. DOI: 10.18632/oncotarget.10523.
Kesch C, Radtke JP, Wintsche A, et al. Correlation between genomic index lesions and mpMRI and 68Ga-PSMA-PET/CT imaging features in primary prostate cancer[J]. Sci Rep, 2018, 8(1): 16708. DOI: 10.1038/s41598-018-35058-3.
Smith CP, Czarniecki M, Mehralivand S, et al. Radiomics and radiogenomics of prostate cancer[J]. Abdom Radiol (NY), 2019, 44(6): 2021-2029. DOI: 10.1007/s00261-018-1660-7.

PREV Application progress of MRI radiomics in the evaluation of liver fibrosis

Tel & Fax: +8610-67113815    E-mail: