Advertisement

Role of Imaging in the Era of Precision Medicine

Published:January 25, 2017DOI:https://doi.org/10.1016/j.acra.2016.11.021
      Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • National Research Council
        • Division on Earth and Life Studies
        • Board on Life Sciences
        • et al.
        Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease.
        The National Academies Press, Washington, DC2011
        • Obama B.
        The precision medicine initiative.
        (Available at) (Accessed October 7, 2015)
        • Jameson J.L.
        • Longo D.L.
        Precision medicine—personalized, problematic, and promising.
        N Engl J Med. 2015; 372: 2229-2234
        • National Human Genome Research Institute
        The cost of sequencing a human genome.
        (Available at) (Accessed November 6, 2016)
        • Hunt S.
        • Jha S.
        Can precision medicine reduce overdiagnosis?.
        Acad Radiol. 2015; 22: 1040-1041
        • European Society of Radiology
        Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR).
        Insights Imaging. 2015; 6: 141-155
        • Herold C.J.
        • Lewin J.S.
        • Wibmer A.G.
        • et al.
        Imaging in the age of precision medicine: summary of the proceedings of the 10th biannual symposium of the International Society for Strategic Studies in Radiology.
        Radiology. 2016; 279: 226-238
        • Mazurowski M.A.
        Radiogenomics: what it is and why it is important.
        J Am Coll Radiol. 2015; 12: 862-866
        • Kumar V.
        • Gu Y.
        • Basu S.
        • et al.
        Radiomics: the process and the challenges.
        Magn Reson Imaging. 2012; 30: 1234-1248
        • Lambin P.
        • Rios-Velazquez E.
        • Leijenaar R.
        • et al.
        Radiomics: extracting more information from medical images using advanced feature analysis.
        Eur J Cancer. 2012; 48: 441-446
        • De Cecco C.N.
        • Ciolina M.
        • Caruso D.
        • et al.
        Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience.
        Abdom Radiol (NY). 2016; 41: 1728-1735
        • Skogen K.
        • Schulz A.
        • Dormagen J.B.
        • et al.
        Diagnostic performance of texture analysis on MRI in grading cerebral gliomas.
        Eur J Radiol. 2016; 85: 824-829
        • Smith A.D.
        • Gray M.R.
        • del Campo S.M.
        • et al.
        Predicting overall survival in patients with metastatic melanoma on antiangiogenic therapy and RECIST stable disease on initial posttherapy images using CT texture analysis.
        AJR Am J Roentgenol. 2015; 205: W283-W293
        • Hayano K.
        • Tian F.
        • Kambadakone A.R.
        • et al.
        Texture analysis of non-contrast-enhanced computed tomography for assessing angiogenesis and survival of soft tissue sarcoma.
        J Comput Assist Tomogr. 2015; 39: 607-612
        • De Cecco C.N.
        • Ganeshan B.
        • Ciolina M.
        • et al.
        Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance.
        Invest Radiol. 2015; 50: 239-245
        • Zhang H.
        • Graham C.M.
        • Elci O.
        • et al.
        Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy.
        Radiology. 2013; 269: 801-809
        • Skogen K.
        • Ganeshan B.
        • Good C.
        • et al.
        Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade.
        J Neurooncol. 2013; 111: 213-219
        • Mankoff D.A.
        A definition of molecular imaging.
        J Nucl Med. 2007; 48 (21N): 18N
        • American Cancer Society
        Cancer Facts & Figures.
        (Available at) (Accessed March 15, 2016)
        • Huber K.E.
        • Carey L.A.
        • Wazer D.E.
        Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy.
        Semin Radiat Oncol. 2009; 19: 204-210
        • Cancer Genome Atlas Network
        Comprehensive molecular portraits of human breast tumours.
        Nature. 2012; 490: 61-70
        • Li H.
        • Zhu Y.
        • Burnside E.S.
        • et al.
        Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.
        NPJ Breast Cancer. 2016; 2: 16012
        • 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
        • Guo W.
        • Li H.
        • Zhu Y.
        • et al.
        Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.
        J Med Imaging (Bellingham). 2015; 2: 041007
        • Zhu Y.
        • Li H.
        • Guo W.
        • et al.
        Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma.
        Sci Rep. 2015; 5: 17787
        • Burnside E.S.
        • Drukker K.
        • Li H.
        • et al.
        Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.
        Cancer. 2016; 122: 748-757
        • 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
        • Chang R.F.
        • Chen H.H.
        • Chang Y.C.
        • et al.
        Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI.
        Magn Reson Imaging. 2016; 34: 809-819
        • Kato F.
        • Kudo K.
        • Yamashita H.
        • et al.
        Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI.
        Eur J Radiol. 2016; 85: 96-102
        • Wang J.
        • Kato F.
        • Oyama-Manabe N.
        • et al.
        Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: a pilot radiomics study.
        PLoS ONE. 2015; 10: e0143308
        • Agner S.C.
        • Rosen M.A.
        • Englander S.
        • et al.
        Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.
        Radiology. 2014; 272: 91-99
        • Chen J.H.
        • Agrawal G.
        • Feig B.
        • et al.
        Triple-negative breast cancer: MRI features in 29 patients.
        Ann Oncol. 2007; 18: 2042-2043
        • 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
        • Bahri S.
        • Chen J.H.
        • Yu H.J.
        • et al.
        Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer?.
        Ann Oncol. 2008; 19: 822-824
        • Sutton E.J.
        • Oh J.H.
        • Dashevsky B.Z.
        • et al.
        Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay.
        J Magn Reson Imaging. 2015; 42: 1398-1406
        • Partridge S.C.
        • Gibbs J.E.
        • Lu Y.
        • et al.
        MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival.
        AJR Am J Roentgenol. 2005; 184: 1774-1781
        • Keyaerts M.
        • Xavier C.
        • Heemskerk J.
        • et al.
        Phase I study of 68Ga-HER2-nanobody for PET/CT assessment of HER2 expression in breast carcinoma.
        J Nucl Med. 2016; 57: 27-33
        • Capala J.
        • Bouchelouche K.
        Molecular imaging of HER2-positive breast cancer: a step toward an individualized “image and treat” strategy.
        Curr Opin Oncol. 2010; 22: 559-566
        • Esserman L.J.
        • Berry D.A.
        • DeMichele A.
        • et al.
        Pathologic complete response predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL—CALGB 150007/150012, ACRIN 6657.
        J Clin Oncol. 2012; 30: 3242-3249
        • Esserman L.J.
        • Berry D.A.
        • Cheang M.C.
        • et al.
        Chemotherapy response and recurrence-free survival in neoadjuvant breast cancer depends on biomarker profiles: results from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657).
        Breast Cancer Res Treat. 2012; 132: 1049-1062
        • Veltman J.
        • Mann R.
        • Kok T.
        • et al.
        Breast tumor characteristics of BRCA1 and BRCA2 gene mutation carriers on MRI.
        Eur Radiol. 2008; 18: 931-938
        • Saslow D.
        • Boetes C.
        • Burke W.
        • et al.
        American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography.
        CA Cancer J Clin. 2007; 57: 75-89
        • Kim M.M.
        • Parolia A.
        • Dunphy M.P.
        • et al.
        Non-invasive metabolic imaging of brain tumours in the era of precision medicine.
        Nat Rev Clin Oncol. 2016; 13: 725-739
        • Hartmann C.
        • Hentschel B.
        • Wick W.
        • et al.
        Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas.
        Acta Neuropathol. 2010; 120: 707-718
        • Emir U.E.
        • Larkin S.J.
        • de Pennington N.
        • et al.
        Noninvasive quantification of 2-hydroxyglutarate in human gliomas with IDH1 and IDH2 mutations.
        Cancer Res. 2016; 76: 43-49
        • Choi C.
        • Ganji S.K.
        • DeBerardinis R.J.
        • et al.
        2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas.
        Nat Med. 2012; 18: 624-629
        • de la Fuente M.I.
        • Young R.J.
        • Rubel J.
        • et al.
        Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma.
        Neuro Oncol. 2016; 18: 283-290
        • Natsumeda M.
        • Igarashi H.
        • Nomura T.
        • et al.
        Accumulation of 2-hydroxyglutarate in gliomas correlates with survival: a study by 3.0-tesla magnetic resonance spectroscopy.
        Acta Neuropathol Commun. 2014; 2: 158
        • Rohle D.
        • Popovici-Muller J.
        • Palaskas N.
        • et al.
        An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells.
        Science. 2013; 340: 626-630
        • Turcan S.
        • Fabius A.W.
        • Borodovsky A.
        • et al.
        Efficient induction of differentiation and growth inhibition in IDH1 mutant glioma cells by the DNMT Inhibitor Decitabine.
        Oncotarget. 2013; 4: 1729-1736
        • Kool M.
        • Korshunov A.
        • Remke M.
        • et al.
        Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas.
        Acta Neuropathol. 2012; 123: 473-484
        • Bluml S.
        • Margol A.S.
        • Sposto R.
        • et al.
        Molecular subgroups of medulloblastoma identification using noninvasive magnetic resonance spectroscopy.
        Neuro Oncol. 2016; 18: 126-131
        • Robinson G.W.
        • Orr B.A.
        • Wu G.
        • et al.
        Vismodegib exerts targeted efficacy against recurrent sonic hedgehog-subgroup medulloblastoma: results from phase II pediatric brain tumor consortium studies PBTC-025B and PBTC-032.
        J Clin Oncol. 2015; 33: 2646-2654
        • Kieran M.W.
        Targeted treatment for sonic hedgehog-dependent medulloblastoma.
        Neuro Oncol. 2014; 16: 1037-1047
        • Thon N.
        • Kunz M.
        • Lemke L.
        • et al.
        Dynamic 18F-FET PET in suspected WHO grade II gliomas defines distinct biological subgroups with different clinical courses.
        Int J Cancer. 2015; 136: 2132-2145
        • Witney T.H.
        • James M.L.
        • Shen B.
        • et al.
        PET imaging of tumor glycolysis downstream of hexokinase through noninvasive measurement of pyruvate kinase M2.
        Sci Transl Med. 2015; 7: 310ra169
        • Mittra E.S.
        • Koglin N.
        • Mosci C.
        • et al.
        Pilot preclinical and clinical evaluation of (4S)-4-(3-[18F]fluoropropyl)-L-glutamate (18F-FSPG) for PET/CT imaging of intracranial malignancies.
        PLoS ONE. 2016; 11: e0148628
        • Koglin N.
        • Mueller A.
        • Berndt M.
        • et al.
        Specific PET imaging of xC—transporter activity using a (1)(8)F-labeled glutamate derivative reveals a dominant pathway in tumor metabolism.
        Clin Cancer Res. 2011; 17: 6000-6011
        • Takeuchi S.
        • Wada K.
        • Toyooka T.
        • et al.
        Increased xCT expression correlates with tumor invasion and outcome in patients with glioblastomas.
        Neurosurgery. 2013; 72 (discussion): 33-41
        • Challapalli A.
        • Aboagye E.O.
        Positron emission tomography imaging of tumor cell metabolism and application to therapy response monitoring.
        Front Oncol. 2016; 6: 44
        • Perreault S.
        • Ramaswamy V.
        • Achrol A.S.
        • et al.
        MRI surrogates for molecular subgroups of medulloblastoma.
        AJNR Am J Neuroradiol. 2014; 35: 1263-1269
        • Carrillo J.A.
        • Lai A.
        • Nghiemphu P.L.
        • et al.
        Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma.
        AJNR Am J Neuroradiol. 2012; 33: 1349-1355
        • Baldock A.L.
        • Yagle K.
        • Born D.E.
        • et al.
        Invasion and proliferation kinetics in enhancing gliomas predict IDH1 mutation status.
        Neuro Oncol. 2014; 16: 779-786
        • Wang K.
        • Wang Y.
        • Fan X.
        • et al.
        Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients.
        Neuro Oncol. 2016; 18: 589-597
        • 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. 2014; 270: 1-2
        • Zinn P.O.
        • Mahajan B.
        • Sathyan P.
        • et al.
        Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme.
        PLoS ONE. 2011; 6: e25451
        • Church T.R.
        • Black W.C.
        • et al.
        • National Lung Screening Trial Research Team
        Results of initial low-dose computed tomographic screening for lung cancer.
        N Engl J Med. 2013; 368: 1980-1991
        • Aberle D.R.
        • Adams A.M.
        • et al.
        • National Lung Screening Trial Research Team
        Reduced lung-cancer mortality with low-dose computed tomographic screening.
        N Engl J Med. 2011; 365: 395-409
        • Henschke C.I.
        • Yankelevitz D.F.
        • et al.
        • International Early Lung Cancer Action Program Investigators
        Survival of patients with stage I lung cancer detected on CT screening.
        N Engl J Med. 2006; 355: 1763-1771
        • Henschke C.I.
        • McCauley D.I.
        • Yankelevitz D.F.
        • et al.
        Early Lung Cancer Action Project: overall design and findings from baseline screening.
        Lancet. 1999; 354: 99-105
        • Paez J.G.
        • Janne P.A.
        • Lee J.C.
        • et al.
        EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy.
        Science. 2004; 304: 1497-1500
        • Soda M.
        • Choi Y.L.
        • Enomoto M.
        • et al.
        Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer.
        Nature. 2007; 448: 561-566
        • Politi K.
        • Herbst R.S.
        Lung cancer in the era of precision medicine.
        Clin Cancer Res. 2015; 21: 2213-2220
        • 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
        • Pao W.
        • Miller V.
        • Zakowski M.
        • et al.
        EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib.
        Proc Natl Acad Sci USA. 2004; 101: 13306-13311
        • Jackman D.M.
        • Yeap B.Y.
        • Sequist L.V.
        • et al.
        Exon 19 deletion mutations of epidermal growth factor receptor are associated with prolonged survival in non-small cell lung cancer patients treated with gefitinib or erlotinib.
        Clin Cancer Res. 2006; 12: 3908-3914
        • Bean J.
        • Brennan C.
        • Shih J.Y.
        • et al.
        MET amplification occurs with or without T790M mutations in EGFR mutant lung tumors with acquired resistance to gefitinib or erlotinib.
        Proc Natl Acad Sci USA. 2007; 104: 20932-20937
        • Pao W.
        • Miller V.A.
        • Politi K.A.
        • et al.
        Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain.
        PLoS Med. 2005; 2: e73
        • Pan Y.
        • Xu Y.
        • Feng S.
        • et al.
        SKLB1206, a novel orally available multikinase inhibitor targeting EGFR activating and T790M mutants, ErbB2, ErbB4, and VEGFR2, displays potent antitumor activity both in vitro and in vivo.
        Mol Cancer Ther. 2012; 11: 952-962
        • Janjigian Y.Y.
        • Azzoli C.G.
        • Krug L.M.
        • et al.
        Phase I/II trial of cetuximab and erlotinib in patients with lung adenocarcinoma and acquired resistance to erlotinib.
        Clin Cancer Res. 2011; 17: 2521-2527
        • Engelman J.A.
        • Zejnullahu K.
        • Gale C.M.
        • et al.
        PF00299804, an irreversible pan-ERBB inhibitor, is effective in lung cancer models with EGFR and ERBB2 mutations that are resistant to gefitinib.
        Cancer Res. 2007; 67: 11924-11932
        • Arcila M.E.
        • Oxnard G.R.
        • Nafa K.
        • et al.
        Rebiopsy of lung cancer patients with acquired resistance to EGFR inhibitors and enhanced detection of the T790M mutation using a locked nucleic acid-based assay.
        Clin Cancer Res. 2011; 17: 1169-1180
        • Marcus D.M.
        • Rossi P.J.
        • Nour S.G.
        • et al.
        The impact of multiparametric pelvic magnetic resonance imaging on risk stratification in patients with localized prostate cancer.
        Urology. 2014; 84: 132-137
        • Schoots I.G.
        • Roobol M.J.
        • Nieboer D.
        • et al.
        Magnetic resonance imaging-targeted biopsy may enhance the diagnostic accuracy of significant prostate cancer detection compared to standard transrectal ultrasound-guided biopsy: a systematic review and meta-analysis.
        Eur Urol. 2015; 68: 438-450
        • Valerio M.
        • Donaldson I.
        • Emberton M.
        • et al.
        Detection of clinically significant prostate cancer using magnetic resonance imaging-ultrasound fusion targeted biopsy: a systematic review.
        Eur Urol. 2015; 68: 8-19
        • Abd-Alazeez M.
        • Ahmed H.U.
        • Arya M.
        • et al.
        The accuracy of multiparametric MRI in men with negative biopsy and elevated PSA level—can it rule out clinically significant prostate cancer?.
        Urol Oncol. 2014; 32 (e17-e22): 45
        • Hoeks C.M.
        • Schouten M.G.
        • Bomers J.G.
        • et al.
        Three-Tesla magnetic resonance-guided prostate biopsy in men with increased prostate-specific antigen and repeated, negative, random, systematic, transrectal ultrasound biopsies: detection of clinically significant prostate cancers.
        Eur Urol. 2012; 62: 902-909
        • Sciarra A.
        • Panebianco V.
        • Cattarino S.
        • et al.
        Multiparametric magnetic resonance imaging of the prostate can improve the predictive value of the urinary prostate cancer antigen 3 test in patients with elevated prostate-specific antigen levels and a previous negative biopsy.
        BJU Int. 2012; 110: 1661-1665
        • Sonn G.A.
        • Chang E.
        • Natarajan S.
        • et al.
        Value of targeted prostate biopsy using magnetic resonance-ultrasound fusion in men with prior negative biopsy and elevated prostate-specific antigen.
        Eur Urol. 2014; 65: 809-815
        • Meng X.
        • Rosenkrantz A.B.
        • Mendhiratta N.
        • et al.
        Relationship between prebiopsy multiparametric magnetic resonance imaging (MRI), biopsy indication, and MRI-ultrasound fusion-targeted prostate biopsy outcomes.
        Eur Urol. 2016; 69: 512-517
        • Numao N.
        • Yoshida S.
        • Komai Y.
        • et al.
        Usefulness of pre-biopsy multiparametric magnetic resonance imaging and clinical variables to reduce initial prostate biopsy in men with suspected clinically localized prostate cancer.
        J Urol. 2013; 190: 502-508
        • Da Rosa M.R.
        • Milot L.
        • Sugar L.
        • et al.
        A prospective comparison of MRI-US fused targeted biopsy versus systematic ultrasound-guided biopsy for detecting clinically significant prostate cancer in patients on active surveillance.
        J Magn Reson Imaging. 2015; 41: 220-225
        • Hu J.C.
        • Chang E.
        • Natarajan S.
        • et al.
        Targeted prostate biopsy in select men for active surveillance: do the Epstein criteria still apply?.
        J Urol. 2014; 192: 385-390
        • Siddiqui M.M.
        • Truong H.
        • Rais-Bahrami S.
        • et al.
        Clinical implications of a multiparametric magnetic resonance imaging based nomogram applied to prostate cancer active surveillance.
        J Urol. 2015; 193: 1943-1949
        • Stoyanova R.
        • Pollack A.
        • Lynne C.
        Using radiogenomics to characterize MRI-guided prostate cancer biopsy heterogenity.
        J Clin Oncol. 2015; 33 (abstr 25)
        • VanderWeele D.J.
        • McCann S.
        • Fan X.
        Radiogenomics of prostate cancer: association between quantitative multiparametric MRI features and PTEN.
        J Clin Oncol. 2015; 33 (abstr 126)
        • Zumsteg Z.S.
        • Spratt D.E.
        • Romesser P.B.
        • et al.
        The natural history and predictors of outcome following biochemical relapse in the dose escalation era for prostate cancer patients undergoing definitive external beam radiotherapy.
        Eur Urol. 2015; 67: 1009-1016
        • Thompson I.M.
        • Valicenti R.K.
        • Albertsen P.
        • et al.
        Adjuvant and salvage radiotherapy after prostatectomy: AUA/ASTRO guideline.
        J Urol. 2013; 190: 441-449
        • Mertan F.V.
        • Lindenberg L.
        • Choyke P.L.
        • et al.
        PET imaging of recurrent and metastatic prostate cancer with novel tracers.
        Future Oncol. 2016; 12: 2463-2477
        • Rowe S.P.
        • Gorin M.A.
        • Allaf M.E.
        • et al.
        PET imaging of prostate-specific membrane antigen in prostate cancer: current state of the art and future challenges.
        Prostate Cancer Prostatic Dis. 2016; 19: 223-230
        • Morigi J.J.
        • Stricker P.D.
        • van Leeuwen P.J.
        • et al.
        Prospective comparison of 18F-fluoromethylcholine versus 68Ga-PSMA PET/CT in prostate cancer patients who have rising PSA after curative treatment and are being considered for targeted therapy.
        J Nucl Med. 2015; 56: 1185-1190
        • Eiber M.
        • Maurer T.
        • Souvatzoglou M.
        • et al.
        Evaluation of hybrid (6)(8)Ga-PSMA ligand PET/CT in 248 patients with biochemical recurrence after radical prostatectomy.
        J Nucl Med. 2015; 56: 668-674
        • Maurer T.
        • Eiber M.
        • Schwaiger M.
        • et al.
        Current use of PSMA-PET in prostate cancer management.
        Nat Rev Urol. 2016; 13: 226-235
        • Shinagare A.B.
        • 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.
        Abdom Imaging. 2015; 40: 1684-1692
        • 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
        • Liu Y.
        • Bai R.
        • Sun H.
        • et al.
        Diffusion-weighted magnetic resonance imaging of uterine cervical cancer.
        J Comput Assist Tomogr. 2009; 33: 858-862
        • Naganawa S.
        • Sato C.
        • Kumada H.
        • et al.
        Apparent diffusion coefficient in cervical cancer of the uterus: comparison with the normal uterine cervix.
        Eur Radiol. 2005; 15: 71-78
        • Sala E.
        • Micco M.
        • Burger I.A.
        • et al.
        Complementary prognostic value of pelvic magnetic resonance imaging and whole-body fluorodeoxyglucose positron emission tomography/computed tomography in the pretreatment assessment of patients with cervical cancer.
        Int J Gynecol Cancer. 2015; 25: 1461-1467
        • Micco M.
        • Vargas H.A.
        • Burger I.A.
        • et al.
        Combined pre-treatment MRI and 18F-FDG PET/CT parameters as prognostic biomarkers in patients with cervical cancer.
        Eur J Radiol. 2014; 83: 1169-1176
        • Burger I.A.
        • Goldman D.A.
        • Vargas H.A.
        • et al.
        Incorporation of postoperative CT data into clinical models to predict 5-year overall and recurrence free survival after primary cytoreductive surgery for advanced ovarian cancer.
        Gynecol Oncol. 2015; 138: 554-559
        • Caobelli F.
        • Alongi P.
        • Evangelista L.
        • et al.
        Predictive value of (18)F-FDG PET/CT in restaging patients affected by ovarian carcinoma: a multicentre study.
        Eur J Nucl Med Mol Imaging. 2016; 43: 404-413
        • Vargas H.A.
        • Micco M.
        • Hong S.I.
        • et al.
        Association between morphologic CT imaging traits and prognostically relevant gene signatures in women with high-grade serous ovarian cancer: a hypothesis-generating study.
        Radiology. 2015; 274: 742-751
        • Thrall J.H.
        Moreton lecture: imaging in the age of precision medicine.
        J Am Coll Radiol. 2015; 12: 1106-1111
        • Hurd M.D.
        • Martorell P.
        • Delavande A.
        • et al.
        Monetary costs of dementia in the United States.
        N Engl J Med. 2013; 368: 1326-1334
        • Bateman R.J.
        • Xiong C.
        • Benzinger T.L.
        • et al.
        Clinical and biomarker changes in dominantly inherited Alzheimer's disease.
        N Engl J Med. 2012; 367: 795-804
        • Bohnen N.I.
        • Djang D.S.
        • Herholz K.
        • et al.
        Effectiveness and safety of 18F-FDG PET in the evaluation of dementia: a review of the recent literature.
        J Nucl Med. 2012; 53: 59-71
        • Dubois B.
        • Feldman H.H.
        • Jacova C.
        • et al.
        Revising the definition of Alzheimer's disease: a new lexicon.
        Lancet Neurol. 2010; 9: 1118-1127
        • Klunk W.E.
        • Engler H.
        • Nordberg A.
        • et al.
        Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B.
        Ann Neurol. 2004; 55: 306-319
        • Li J.
        • Shi Y.
        • Toga A.W.
        Mapping brain anatomical connectivity using diffusion magnetic resonance imaging: structural connectivity of the human brain.
        IEEE Signal Process Mag. 2016; 33: 36-51
        • Brown J.A.
        • Terashima K.H.
        • Burggren A.C.
        • et al.
        Brain network local interconnectivity loss in aging APOE-4 allele carriers.
        Proc Natl Acad Sci USA. 2011; 108: 20760-20765
        • Roussotte F.F.
        • Daianu M.
        • Jahanshad N.
        • et al.
        Neuroimaging and genetic risk for Alzheimer's disease and addiction-related degenerative brain disorders.
        Brain Imaging Behav. 2014; 8: 217-233
        • Jahanshad N.
        • Rajagopalan P.
        • Hua X.
        • et al.
        Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity.
        Proc Natl Acad Sci USA. 2013; 110: 4768-4773
        • Bralten J.
        • Arias-Vasquez A.
        • Makkinje R.
        • et al.
        Association of the Alzheimer's gene SORL1 with hippocampal volume in young, healthy adults.
        Am J Psychiatry. 2011; 168: 1083-1089
        • Potkin S.G.
        • Guffanti G.
        • Lakatos A.
        • et al.
        Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer's disease.
        PLoS ONE. 2009; 4: e6501
        • Ebinger M.
        • Winter B.
        • Wendt M.
        • et al.
        Effect of the use of ambulance-based thrombolysis on time to thrombolysis in acute ischemic stroke: a randomized clinical trial.
        JAMA. 2014; 311: 1622-1631
        • Kidwell C.S.
        • Jahan R.
        • Gornbein J.
        • et al.
        A trial of imaging selection and endovascular treatment for ischemic stroke.
        N Engl J Med. 2013; 368: 914-923
        • Demchuk A.M.
        • Dowlatshahi D.
        • Rodriguez-Luna D.
        • et al.
        Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign (PREDICT): a prospective observational study.
        Lancet Neurol. 2012; 11: 307-314
        • Du F.Z.
        • Jiang R.
        • Gu M.
        • et al.
        The accuracy of spot sign in predicting hematoma expansion after intracerebral hemorrhage: a systematic review and meta-analysis.
        PLoS ONE. 2014; 9: e115777
        • Ivanidze J.
        • Kesavabhotla K.
        • Kallas O.N.
        • et al.
        Evaluating blood-brain barrier permeability in delayed cerebral infarction after aneurysmal subarachnoid hemorrhage.
        AJNR Am J Neuroradiol. 2015; 36: 850-854
        • Jones S.
        • Anagnostou V.
        • Lytle K.
        • et al.
        Personalized genomic analyses for cancer mutation discovery and interpretation.
        Sci Transl Med. 2015; 7: 283ra53
        • Lipinski K.A.
        • Barber L.J.
        • Davies M.N.
        • et al.
        Cancer evolution and the limits of predictability in precision cancer medicine.
        Trends Cancer. 2016; 2: 49-63
        • Hoefnagel L.D.
        • van der Groep P.
        • van de Vijver M.J.
        • et al.
        Discordance in ERalpha, PR and HER2 receptor status across different distant breast cancer metastases within the same patient.
        Ann Oncol. 2013; 24: 3017-3023
        • Gerlinger M.
        • Rowan A.J.
        • Horswell S.
        • et al.
        Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.
        N Engl J Med. 2012; 366: 883-892
        • Shah S.P.
        • Morin R.D.
        • Khattra J.
        • et al.
        Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution.
        Nature. 2009; 461: 809-813
        • Woff E.
        • Hendlisz A.
        • Garcia C.
        • et al.
        Monitoring metabolic response using FDG PET-CT during targeted therapy for metastatic colorectal cancer.
        Eur J Nucl Med Mol Imaging. 2016; 43: 1792-1801
        • Hricak H.
        Oncologic imaging: a guiding hand of personalized cancer care.
        Radiology. 2011; 259: 633-640
        • Huyge V.
        • Garcia C.
        • Alexiou J.
        • et al.
        Heterogeneity of metabolic response to systemic therapy in metastatic breast cancer patients.
        Clin Oncol. 2010; 22: 818-827
        • Doumou G.
        • Siddique M.
        • Tsoumpas C.
        • et al.
        The precision of textural analysis in (18)F-FDG-PET scans of oesophageal cancer.
        Eur Radiol. 2015; 25: 2805-2812
        • Cook G.J.
        • O'Brien M.E.
        • Siddique M.
        • et al.
        Non-small cell lung cancer treated with Erlotinib: heterogeneity of (18)F-FDG uptake at PET-association with treatment response and prognosis.
        Radiology. 2015; 276: 883-893
        • Cook G.J.
        • Yip C.
        • Siddique M.
        • et al.
        Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?.
        J Nucl Med. 2013; 54: 19-26
        • Chicklore S.
        • Goh V.
        • Siddique M.
        • et al.
        Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis.
        Eur J Nucl Med Mol Imaging. 2013; 40: 133-140
        • Alyass A.
        • Turcotte M.
        • Meyre D.
        From big data analysis to personalized medicine for all: challenges and opportunities.
        BMC Med Genomics. 2015; 8: 33
        • Van Neste L.
        • Van Criekinge W.
        We are all individuals… bioinformatics in the personalized medicine era.
        Cell Oncol. 2015; 38: 29-37
        • Abramson R.G.
        • Burton K.R.
        • Yu J.P.
        • et al.
        Methods and challenges in quantitative imaging biomarker development.
        Acad Radiol. 2015; 22: 25-32
        • Rosenkrantz A.B.
        • Mendiratta-Lala M.
        • Bartholmai B.J.
        • et al.
        Clinical utility of quantitative imaging.
        Acad Radiol. 2015; 22: 33-49