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Review Article| Volume 1, P71-82, September 2019

Radiogenomics of Oncology

Current Trends and Future Directions
      Radiogenomics aims to identify potential associations between clinical imaging and cellular and molecular profiling to characterize disease phenotypes.

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      References

        • Kuo M.D.
        • Jamshidi N.
        Behind the numbers: decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations.
        Radiology. 2014; 270: 320-325
        • Louis D.N.
        • Perry A.
        • Reifenberger G.
        • et al.
        The 2016 World Health Organization classification of tumors of the central nervous system: a summary.
        Acta Neuropathol (Berl). 2016; 131: 803-820
        • Colen R.R.
        • Hassan I.
        • Elshafeey N.
        • et al.
        Shedding light on the 2016 World Health Organization classification of tumors of the central nervous system in the era of radiomics and radiogenomics.
        Magn Reson Imaging Clin N Am. 2016; 24: 741-749
        • Diehn M.
        • Nardini C.
        • Wang D.S.
        • et al.
        Identification of noninvasive imaging surrogates for brain tumor gene-expression modules.
        Proc Natl Acad Sci U S A. 2008; 105: 5213-5218
        • Gutman D.A.
        • Cooper L.A.D.
        • Hwang S.N.
        • et al.
        MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.
        Radiology. 2013; 267: 560-569
        • Jamshidi N.
        • Diehn M.
        • Bredel M.
        • et al.
        Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation.
        Radiology. 2013; 270: 1-2
        • Jain R.
        • Poisson L.M.
        • Gutman D.
        • et al.
        Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor.
        Radiology. 2014; 272: 484-493
        • Rao A.
        • Rao G.
        • Gutman D.A.
        • et al.
        A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma.
        J Neurosurg. 2016; 124: 1008-1017
        • Zinn P.O.
        • Mahajan B.
        • Majadan B.
        • et al.
        Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme.
        PLoS One. 2011; 6: e25451
        • Gevaert O.
        • Mitchell L.A.
        • Achrol A.S.
        • et al.
        Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features.
        Radiology. 2015; 276: 313
        • Fukushima T.
        • Takeshima H.
        • Kataoka H.
        Anti-glioma therapy with temozolomide and status of the DNA-repair gene MGMT.
        Anticancer Res. 2009; 29: 4845-4854
        • Zhang J.
        • Stevens M.F.G.
        • Laughton C.A.
        • et al.
        Acquired resistance to temozolomide in glioma cell lines: molecular mechanisms and potential translational applications.
        Oncology. 2010; 78: 103-114
        • Pope W.B.
        • Lai A.
        • Mehta R.
        • et al.
        Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma.
        AJNR Am J Neuroradiol. 2011; 32: 882-889
        • Brandes A.A.
        • Franceschi E.
        • Tosoni A.
        • et al.
        MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients.
        J Clin Oncol. 2008; 26: 2192-2197
        • Qian X.
        • Tan H.
        • Zhang J.
        • et al.
        Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study.
        Oncotarget. 2016; 7: 55377-55394
        • Yan H.
        • Parsons D.W.
        • Jin G.
        • et al.
        IDH1 and IDH2 mutations in gliomas.
        N Engl J Med. 2009; 360: 765-773
        • Villa C.
        • Miquel C.
        • Mosses D.
        • et al.
        The 2016 World Health Organization classification of tumours of the central nervous system.
        Presse Med. 2018; 47: e187-e200
        • Pope W.B.
        • Prins R.M.
        • Albert Thomas M.
        • et al.
        Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy.
        J Neurooncol. 2012; 107: 197-205
        • Andronesi O.C.
        • Rapalino O.
        • Gerstner E.
        • et al.
        Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate.
        J Clin Invest. 2013; 123: 3659-3663
        • Jemal A.
        • Siegel R.
        • Xu J.
        • et al.
        Cancer statistics, 2010.
        CA Cancer J Clin. 2010; 60: 277-300
        • Gevaert O.
        • Xu J.
        • Hoang C.D.
        • et al.
        Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results.
        Radiology. 2012; 264: 387-396
        • Zhou M.
        • Leung A.
        • Echegaray S.
        • et al.
        Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications.
        Radiology. 2018; 286: 307-315
        • Shaw A.T.
        • Yeap B.Y.
        • Solomon B.J.
        • et al.
        Effect of crizotinib on overall survival in patients with advanced non-small-cell lung cancer harbouring ALK gene rearrangement: a retrospective analysis.
        Lancet Oncol. 2011; 12: 1004-1012
        • Yamamoto S.
        • Korn R.L.
        • Oklu R.
        • et al.
        ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization.
        Radiology. 2014; 272: 568-576
        • Kim T.J.
        • Lee C.-T.
        • Jheon S.H.
        • et al.
        Radiologic characteristics of surgically resected non-small cell lung cancer with ALK rearrangement or EGFR mutations.
        Ann Thorac Surg. 2016; 101: 473-480
        • Antonicelli A.
        • Cafarotti S.
        • Indini A.
        • et al.
        EGFR-targeted therapy for non-small cell lung cancer: focus on EGFR oncogenic mutation.
        Int J Med Sci. 2013; 10: 320-330
        • Ozkan E.
        • West A.
        • Dedelow J.A.
        • et al.
        CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung.
        AJR Am J Roentgenol. 2015; 205: 1016-1025
        • Liu Y.
        • Kim J.
        • Qu F.
        • et al.
        CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.
        Radiology. 2016; 280: 271-280
        • Koo H.J.
        • Kim M.Y.
        • Park S.
        • et al.
        Non-small cell lung cancer with resistance to EGFR-TKI therapy: CT characteristics of T790M mutation-positive cancer.
        Radiology. 2018; 289: 227-237
        • Hsu J.-S.
        • Huang M.-S.
        • Chen C.-Y.
        • et al.
        Correlation between EGFR mutation status and computed tomography features in patients with advanced pulmonary adenocarcinoma.
        J Thorac Imaging. 2014; 29: 357-363
        • Hong S.J.
        • Kim T.J.
        • Choi Y.W.
        • et al.
        Radiogenomic correlation in lung adenocarcinoma with epidermal growth factor receptor mutations: imaging features and histological subtypes.
        Eur Radiol. 2016; 26: 3660-3668
        • Nair V.S.
        • Gevaert O.
        • Davidzon G.
        • et al.
        Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer.
        Cancer Res. 2012; 72: 3725-3734
        • Kang Y.
        • Massagué J.
        Epithelial-mesenchymal transitions: twist in development and metastasis.
        Cell. 2004; 118: 277-279
        • Singh A.
        • Settleman J.
        EMT, cancer stem cells and drug resistance: an emerging axis of evil in the war on cancer.
        Oncogene. 2010; 29: 4741-4751
        • Yamamoto S.
        • Huang D.
        • Du L.
        • et al.
        Radiogenomic analysis demonstrates associations between (18)F-fluoro-2-deoxyglucose PET, prognosis, and epithelial-mesenchymal transition in non-small cell lung cancer.
        Radiology. 2016; 280: 261-270
        • Jamshidi N.
        • Huang D.
        • Abtin F.G.
        • et al.
        Genomic adequacy from solid tumor core needle biopsies of ex vivo tissue and in vivo lung masses: prospective study.
        Radiology. 2016; 282: 903-912
        • Jeong W.K.
        • Jamshidi N.
        • Felker E.R.
        • et al.
        Radiomics and radiogenomics of primary liver cancers.
        Clin Mol Hepatol. 2018; 25: 21-29
        • Elsayes K.M.
        • Hooker J.C.
        • Agrons M.M.
        • et al.
        2017 version of LI-RADS for CT and MR imaging: an update.
        Radiographics. 2017; 37: 1994-2017
        • Segal E.
        • Sirlin C.B.
        • Ooi C.
        • et al.
        Decoding global gene expression programs in liver cancer by noninvasive imaging.
        Nat Biotechnol. 2007; 25: 675-680
        • Kuo M.D.
        • Gollub J.
        • Sirlin C.B.
        • et al.
        Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma.
        J Vasc Interv Radiol. 2007; 18: 821-830
        • Taouli B.
        • Hoshida Y.
        • Kakite S.
        • et al.
        Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results.
        Eur Radiol. 2017; 27: 4472-4481
        • Banerjee S.
        • Wang D.S.
        • Kim H.J.
        • et al.
        A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma.
        Hepatology. 2015; 62: 792-800
        • Bakr S.
        • Echegaray S.
        • Shah R.
        • et al.
        Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study.
        J Med Imaging (Bellingham). 2017; 4: 041303
        • Chen S.
        • Zhu Y.
        • Liu Z.
        • et al.
        Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: a retrospective pilot study.
        Eur J Radiol. 2017; 90: 198-204
        • Zhou Y.
        • He L.
        • Huang Y.
        • et al.
        CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma.
        Abdom Radiol. 2017; 42: 1695-1704
        • Renzulli M.
        • Brocchi S.
        • Cucchetti A.
        • et al.
        Can current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma?.
        Radiology. 2016; 279: 432-442
        • Zheng B.-H.
        • Liu L.-Z.
        • Zhang Z.-Z.
        • et al.
        Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients.
        BMC Cancer. 2018; 18 (Available at:): 1148
        • Yoshikawa D.
        • Ojima H.
        • Iwasaki M.
        • et al.
        Clinicopathological and prognostic significance of EGFR, VEGF, and HER2 expression in cholangiocarcinoma.
        Br J Cancer. 2008; 98: 418-425
        • Thelen A.
        • Scholz A.
        • Weichert W.
        • et al.
        Tumor-associated angiogenesis and lymphangiogenesis correlate with progression of intrahepatic cholangiocarcinoma.
        Am J Gastroenterol. 2010; 105: 1123-1132
        • Sadot E.
        • Simpson A.L.
        • Do R.K.G.
        • et al.
        Cholangiocarcinoma: correlation between molecular profiling and imaging phenotypes.
        PLoS One. 2015; 10: e0132953
        • Reznek R.H.
        CT/MRI in staging renal cell carcinoma.
        Cancer Imaging. 2004; 4 Spec No A: S25-S32
        • Shinagare A.B.
        • Krajewski K.M.
        • Braschi-Amirfarzan M.
        • et al.
        Advanced renal cell carcinoma: role of the radiologist in the era of precision medicine.
        Radiology. 2017; 284: 333-351
        • Choueiri T.K.
        • Plantade A.
        • Elson P.
        • et al.
        Efficacy of sunitinib and sorafenib in metastatic papillary and chromophobe renal cell carcinoma.
        J Clin Oncol. 2008; 26: 127-131
        • Karlo C.A.
        • Di Paolo P.L.
        • Chaim J.
        • et al.
        Radiogenomics of clear-cell renal cell carcinoma: associations between CT imaging features and mutations.
        Radiology. 2014; 270: 464-471
        • Hakimi A.A.
        • Chen Y.-B.
        • Wren J.
        • et al.
        Clinical and pathologic impact of select chromatin-modulating tumor suppressors in clear cell renal cell carcinoma.
        Eur Urol. 2013; 63: 848-854
        • Hindman N.M.
        • Bosniak M.A.
        • Rosenkrantz A.B.
        • et al.
        Multilocular cystic renal cell carcinoma: comparison of imaging and pathologic findings.
        AJR Am J Roentgenol. 2012; 198: W20-W26
        • 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.
        Radiology. 2015; 277: 114-123
        • Jonasch E.
        • Wood C.G.
        • Matin S.F.
        • et al.
        Phase II presurgical feasibility study of bevacizumab in untreated patients with metastatic renal cell carcinoma.
        J Clin Oncol. 2009; 27: 4076-4081
        • Jamshidi N.
        • Jonasch E.
        • Zapala M.
        • et al.
        The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial.
        Eur Radiol. 2016; 26: 2798-2807
        • George S.
        • Rini B.I.
        • Hammers H.J.
        Emerging role of combination immunotherapy in the first-line treatment of advanced renal cell carcinoma: a review.
        JAMA Oncol. 2019; 5: 411-421.
        • Hirsch M.S.
        • Signoretti S.
        • Dal Cin P.
        Adult renal cell carcinoma: a review of established entities from morphology to molecular genetics.
        Surg Pathol Clin. 2015; 8: 587-621
      1. Eble J. Epstein J. Sesterhenn I. Pathology and genetics of tumours of the urinary system and male genital organs. WHO classification of tumours. 1st edition. IARC, Lyon (France)2004: 359
        • Davis C.J.
        • Mostofi F.K.
        • Sesterhenn I.A.
        Renal medullary carcinoma the seventh sickle cell nephropathy.
        Am J Surg Pathol. 1995; 19: 1-11
        • Weiner A.B.
        • Patel S.G.
        • Eggener S.E.
        Pathologic outcomes for low-risk prostate cancer after delayed radical prostatectomy in the United States.
        Urol Oncol. 2015; 33: 164.e11-7
        • O’Brien D.
        • Loeb S.
        • Carvalhal G.F.
        • et al.
        Delay of surgery in men with low risk prostate cancer.
        J Urol. 2011; 185: 2143-2147
        • Weinreb J.C.
        • Barentsz J.O.
        • Choyke P.L.
        • et al.
        PI-RADS prostate imaging - reporting and data system: 2015, version 2.
        Eur Urol. 2016; 69: 16-40
        • Klein E.A.
        • Cooperberg M.R.
        • Magi-Galluzzi C.
        • et al.
        A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling.
        Eur Urol. 2014; 66: 550-560
        • Magi-Galluzzi C.
        • Maddala T.
        • Falzarano S.M.
        • et al.
        Gene expression in normal-appearing tissue adjacent to prostate cancers are predictive of clinical outcome: evidence for a biologically meaningful field effect.
        Oncotarget. 2016; 7: 33855-33865
        • 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.
        Oncotarget. 2016; 7: 53362-53376
        • Jamshidi N.
        • Margolis D.J.
        • Raman S.
        • et al.
        Multiregional radiogenomic assessment of prostate microenvironments with multiparametric MR imaging and DNA whole-exome sequencing of prostate glands with adenocarcinoma1.
        Radiology. 2017; 284: 109-119
        • Siddiqui M.M.
        • Rais-Bahrami S.
        • Turkbey B.
        • et al.
        Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.
        JAMA. 2015; 313: 390-397
        • Soodana-Prakash N.
        • Stoyanova R.
        • Bhat A.
        • et al.
        Entering an era of radiogenomics in prostate cancer risk stratification.
        Transl Androl Urol. 2018; 7: S443-S452
        • Lee A.Y.
        • Ichikawa L.
        • Lee J.M.
        • et al.
        Concordance of BI-RADS assessments and management recommendations for breast MRI in community practice.
        AJR Am J Roentgenol. 2016; 206: 211-216
        • Sørlie T.
        • Perou C.M.
        • Tibshirani R.
        • et al.
        Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.
        Proc Natl Acad Sci U S A. 2001; 98: 10869-10874
        • van ’t Veer L.J.
        • Dai H.
        • van de Vijver M.J.
        • et al.
        Gene expression profiling predicts clinical outcome of breast cancer.
        Nature. 2002; 415: 530-536
        • Yamamoto S.
        • Maki D.D.
        • Korn R.L.
        • et al.
        Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape.
        AJR Am J Roentgenol. 2012; 199: 654-663
        • Yamamoto S.
        • Han W.
        • Kim Y.
        • et al.
        Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis.
        Radiology. 2015; 275: 384-392
        • Li H.
        • Zhu Y.
        • Burnside E.S.
        • 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.
        Radiology. 2016; 281: 382-391
        • Woodard G.A.
        • Ray K.M.
        • Joe B.N.
        • et al.
        Qualitative radiogenomics: association between oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features.
        Radiology. 2018; 286: 60-70
        • Blaschke E.
        • Abe H.
        MRI phenotype of breast cancer: kinetic assessment for molecular subtypes.
        J Magn Reson Imaging. 2015; 42: 920-924
        • Mazurowski M.A.
        • Zhang J.
        • Grimm L.J.
        • et al.
        Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
        Radiology. 2014; 273: 365-372
        • Kim E.J.
        • Kim S.H.
        • Park G.E.
        • et al.
        Histogram analysis of apparent diffusion coefficient at 3.0t: correlation with prognostic factors and subtypes of invasive ductal carcinoma.
        J Magn Reson Imaging. 2015; 42: 1666-1678
        • Park S.H.
        • Choi H.-Y.
        • Hahn S.Y.
        Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla.
        J Magn Reson Imaging. 2015; 41: 175-182
        • Martincich L.
        • Deantoni V.
        • Bertotto I.
        • et al.
        Correlations between diffusion-weighted imaging and breast cancer biomarkers.
        Eur Radiol. 2012; 22: 1519-1528
        • Waugh S.A.
        • Purdie C.A.
        • Jordan L.B.
        • et al.
        Magnetic resonance imaging texture analysis classification of primary breast cancer.
        Eur Radiol. 2016; 26: 322-330
        • Grimm L.J.
        • Zhang J.
        • Mazurowski M.A.
        Computational approach to radiogenomics of breast cancer: luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.
        J Magn Reson Imaging. 2015; 42: 902-907
        • Sutton E.J.
        • Dashevsky B.Z.
        • Oh J.H.
        • et al.
        Breast cancer molecular subtype classifier that incorporates MRI features.
        J Magn Reson Imaging. 2016; 44: 122-129
        • Ashraf A.B.
        • Daye D.
        • Gavenonis S.
        • et al.
        Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.
        Radiology. 2014; 272: 374-384
        • Ashraf A.B.
        • Gavenonis S.C.
        • Daye D.
        • et al.
        A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk.
        IEEE Trans Med Imaging. 2013; 32: 637-648
        • Mahajan A.
        • Deshpande S.S.
        • Thakur M.H.
        Diffusion magnetic resonance imaging: a molecular imaging tool caught between hope, hype and the real world of “personalized oncology.
        World J Radiol. 2017; 9: 253-268
        • Brix N.
        • Tiefenthaller A.
        • Anders H.
        • et al.
        Abscopal, immunological effects of radiotherapy: narrowing the gap between clinical and preclinical experiences.
        Immunol Rev. 2017; 280: 249-279
        • Lencioni R.
        • Cioni D.
        • Crocetti L.
        • et al.
        Early-stage hepatocellular carcinoma in patients with cirrhosis: long-term results of percutaneous image-guided radiofrequency ablation.
        Radiology. 2005; 234: 961-967
        • Ahmed M.
        • Kumar G.
        • Moussa M.
        • et al.
        Hepatic radiofrequency ablation-induced stimulation of distant tumor growth is suppressed by c-Met inhibition.
        Radiology. 2016; 279: 103-117
        • Rozenblum N.
        • Zeira E.
        • Scaiewicz V.
        • et al.
        Oncogenesis: an “off-target” effect of radiofrequency ablation.
        Radiology. 2015; 276: 426-432
        • Ahmed M.
        • Kumar G.
        • Navarro G.
        • et al.
        Systemic siRNA nanoparticle-based drugs combined with radiofrequency ablation for cancer therapy.
        PLoS One. 2015; 10: e0128910
        • Kumar G.
        • Goldberg S.N.
        • Gourevitch S.
        • et al.
        Targeting STAT3 to suppress systemic pro-oncogenic effects from hepatic radiofrequency ablation.
        Radiology. 2017; 286: 524-536
        • Tam A.L.
        • Lim H.J.
        • Wistuba II,
        • et al.
        Image-guided biopsy in the era of personalized cancer care: proceedings from the society of interventional radiology research consensus panel.
        J Vasc Interv Radiol. 2016; 27: 8-19