Big Data and the Future of Radiology Informatics

Published:November 05, 2015DOI:
      Rapid growth in the amount of data that is electronically recorded as part of routine clinical operations has generated great interest in the use of Big Data methodologies to address clinical and research questions. These methods can efficiently analyze and deliver insights from high-volume, high-variety, and high-growth rate datasets generated across the continuum of care, thereby forgoing the time, cost, and effort of more focused and controlled hypothesis-driven research. By virtue of an existing robust information technology infrastructure and years of archived digital data, radiology departments are particularly well positioned to take advantage of emerging Big Data techniques. In this review, we describe four areas in which Big Data is poised to have an immediate impact on radiology practice, research, and operations. In addition, we provide an overview of the Big Data adoption cycle and describe how academic radiology departments can promote Big Data development.

      Key Words

      Abbreviations and Acronyms:

      EMR (electronic medical record), MCI (mild cognitive impairment), ADNI (Alzheimer's Disease Neuroimaging Initiative), PE (pulmonary embolism), CTA (computed tomography angiography), RIS (radiology information system), PACS (picture archiving and communication system), ACR (American College of Radiology), HIPAA (Health Insurance Portability and Accountability Act)
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        • Murdoch T.B.
        • Detsky A.S.
        The inevitable application of big data to health care.
        JAMA. 2013; 309: 1351-1352
        • Badawi O.
        • Brennan T.
        • Celi L.A.
        • et al.
        Making big data useful for health care: a summary of the inaugural MIT critical data conference.
        JMIR Med Inform. 2014; 2: e22
        • McAfee A.
        • Brynjolfsson E.
        Big Data: The Management Revolution.
        (Harvard Business Review)2012
        • Schroeck M.
        • Schockley R.
        Analytics: The real-world use of big data.
        (Available at:) (Accessed June 3, 2015)
        • Groves P.
        • Kayyali B.
        • Knott D.
        • et al.
        McKinsey report “The Big Data Revolution in Healthcare.”.
        (Available at:) (Accessed June 3, 2015)
        • Olsen L.
        • Aisner D.
        • McGinnis J.M.
        • et al.
        in: The learning healthcare system: workshop summary. National Academies Press, Washington, DC2007: 1-36
        • Blount M.
        • Ebling M.R.
        • Eklund J.M.
        • et al.
        Real-time analysis for intensive care: development and deployment of the Artemis analytic system.
        IEEE Eng Med Biol Mag. 2010; 29: 110-118
        • Avrin D.E.
        • Urbania T.H.
        Demise of film.
        Acad Radiol. 2014; 21: 303-304
        • Levine D.
        • Brown D.L.
        • Andreotti R.F.
        • et al.
        Management of asymptomatic ovarian and other adnexal cysts imaged at US: Society of Radiologists in Ultrasound Consensus Conference Statement.
        Radiology. 2010; 256: 943-954
        • Mainiero M.B.
        • Lourenco A.
        • Mahoney M.C.
        • et al.
        ACR appropriateness criteria breast cancer screening.
        J Am Coll Radiol. 2013; 10: 11-14
        • Walker C.
        • Haylock B.
        • Husband D.
        • et al.
        Clinical use of genotype to predict chemosensitivity in oligodendroglial tumors.
        Neurology. 2006; 66: 1661-1667
        • 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
        • Dawood S.
        • Broglio K.
        • Buzdar A.U.
        • et al.
        Prognosis of women with metastatic breast cancer by HER2 status and trastuzumab treatment: an institutional-based review.
        J Clin Oncol. 2010; 28: 92-98
        • Dilsizian S.E.
        • Siegel E.L.
        Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.
        Curr Cardiol Rep. 2014; 16: 441
        • Howlader N.
        • Noone A.M.
        • Krapcho M.
        • et al.
        SEER Cancer Statistics Review, 1975-2012.
        (Bethesda, MD: National Cancer Institute; Available at:) (Accessed June 3, 2015)
        • MacMahon H.
        • Austin J.H.
        • Gamsu G.
        • et al.
        Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society.
        Radiology. 2005; 237: 395-400
        • Morrison J.J.
        • Hostetter J.
        • Wang K.
        • et al.
        Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.
        J Digit Imaging. 2015; 28: 18-23
        • McClelland R.L.
        • Nasir K.
        • Budoff M.
        • et al.
        Arterial age as a function of coronary artery calcium (from the Multi-Ethnic Study of Atherosclerosis [MESA]).
        Am J Cardiol. 2009; 103: 59-63
        • Malin J.L.
        Envisioning Watson as a rapid-learning system for oncology.
        J Oncol Pract. 2013; 9: 155-157
        • Pyra T.
        • Hui B.
        • Hanstock C.
        • et al.
        Combined structural and neurochemical evaluation of the corticospinal tract in amyotrophic lateral sclerosis.
        Amyotroph Lateral Scler. 2010; 11: 157-165
        • Landry G.J.
        • Liu-Ambrose T.
        Buying time: a rationale for examining the use of circadian rhythm and sleep interventions to delay progression of mild cognitive impairment to Alzheimer's disease.
        Front Aging Neurosci. 2014; 6: 325
        • Hebert L.E.
        • Weuve J.
        • Scherr P.A.
        • et al.
        Alzheimer disease in the United States (2010-2050) estimated using the 2010 census.
        Neurology. 2013; 80: 1778-1783
        • Langa K.M.
        • Levine D.A.
        The diagnosis and management of mild cognitive impairment: a clinical review.
        JAMA. 2014; 312: 2551-2561
        • McEvoy L.K.
        • Holland D.
        • Hagler Jr, D.J.
        • et al.
        Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis.
        Radiology. 2011; 259: 834-843
        • 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
        • Aerts H.J.
        • Velazquez E.R.
        • Leijenaar R.T.
        • et al.
        Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
        Nat Commun. 2014; 5: 4006
        • Mazurowski M.A.
        Radiogenomics: what it is and why it is important.
        J Am Coll Radiol. 2015; 12: 862-866
        • Rosenstein B.S.
        • West C.M.
        • Bentzen S.M.
        • et al.
        Radiogenomics: radiobiology enters the era of big data and team science.
        Int J Radiat Oncol Biol Phys. 2014; 89: 709-713
        • Porter M.E.
        A strategy for health care reform—toward a value-based system.
        N Engl J Med. 2009; 361: 109-112
        • Porter M.E.
        What is value in health care?.
        N Engl J Med. 2010; 363: 2477-2481
        • Bossuyt P.M.
        • Reitsma J.B.
        • Linnet K.
        • et al.
        Beyond diagnostic accuracy: the clinical utility of diagnostic tests.
        Clin Chem. 2012; 58: 1636-1643
        • Ferrante di Ruffano L.
        • Hyde C.J.
        • McCaffery K.J.
        • et al.
        Assessing the value of diagnostic tests: a framework for designing and evaluating trials.
        BMJ. 2012; 344: e686
        • Heit J.
        • Cohen A.
        • Anderson F.J.
        Estimated annual number of incident and recurrent, non-fatal and fatal venous thromboembolism (VTE) events in the US.
        Blood. 2005; 106: 267A
        • LaMori J.C.
        • Shoheiber O.
        • Mody S.H.
        • et al.
        Inpatient resource use and cost burden of deep vein thrombosis and pulmonary embolism in the United States.
        Clin Ther. 2015; 37: 62-70
        • Remy-Jardin M.
        • Remy J.
        • Wattinne L.
        • et al.
        Central pulmonary thromboembolism: diagnosis with spiral volumetric CT with the single-breath-hold technique—comparison with pulmonary angiography.
        Radiology. 1992; 185: 381-387
        • Anderson D.R.
        • Kovacs M.J.
        • Dennie C.
        • et al.
        Use of spiral computed tomography contrast angiography and ultrasonography to exclude the diagnosis of pulmonary embolism in the emergency department.
        J Emerg Med. 2005; 29: 399-404
        • Bettmann M.A.
        • Baginski S.G.
        • White R.D.
        • et al.
        ACR Appropriateness Criteria(R) acute chest pain—suspected pulmonary embolism.
        J Thorac Imaging. 2012; 27: W28-W31
        • Costantino M.M.
        • Randall G.
        • Gosselin M.
        • et al.
        CT angiography in the evaluation of acute pulmonary embolus.
        AJR Am J Roentgenol. 2008; 191: 471-474
        • Wiener R.S.
        • Schwartz L.M.
        • Woloshin S.
        Time trends in pulmonary embolism in the United States: evidence of overdiagnosis.
        Arch Intern Med. 2011; 171: 831-837
        • Khorasani R.
        You should eliminate paper from your PACS workflow: why and how?.
        J Am Coll Radiol. 2006; 3: 628-629
        • Prevedello L.
        • Khorasani R.
        Which radiology application drives your workflow? Which should?.
        J Am Coll Radiol. 2013; 10: 80-82
        • Nagy P.G.
        • Warnock M.J.
        • Daly M.
        • et al.
        Informatics in radiology: automated Web-based graphical dashboard for radiology operational business intelligence.
        Radiographics. 2009; 29: 1897-1906
        • Lu L.
        • Li J.
        • Gisler P.
        Improving financial performance by modeling and analysis of radiology procedure scheduling at a large community hospital.
        J Med Syst. 2011; 35: 299-307
        • Lakhani P.
        • Kim W.
        • Langlotz C.P.
        Automated detection of critical results in radiology reports.
        J Digit Imaging. 2012; 25: 30-36
        • Bhargavan M.
        • Sunshine J.H.
        Utilization of radiology services in the United States: levels and trends in modalities, regions, and populations.
        Radiology. 2005; 234: 824-832
        • Parker L.
        • Levin D.C.
        • Frangos A.
        • et al.
        Geographic variation in the utilization of noninvasive diagnostic imaging: national medicare data, 1998-2007.
        AJR Am J Roentgenol. 2010; 194: 1034-1039
        • Patti J.A.
        The national radiology data registry: a necessary component of quality health care.
        J Am Coll Radiol. 2011; 8: 453
        • Rosen M.P.
        • Ding A.
        • Blake M.A.
        • et al.
        ACR Appropriateness Criteria(R) right lower quadrant pain—suspected appendicitis.
        J Am Coll Radiol. 2011; 8: 749-755
        • Trout A.T.
        • Sanchez R.
        • Ladino-Torres M.F.
        • et al.
        A critical evaluation of US for the diagnosis of pediatric acute appendicitis in a real-life setting: how can we improve the diagnostic value of sonography?.
        Pediatr Radiol. 2012; 42: 813-823
        • Abujudeh H.
        • Pyatt Jr, R.S.
        • Bruno M.A.
        • et al.
        RADPEER peer review: relevance, use, concerns, challenges, and direction forward.
        J Am Coll Radiol. 2014; 11: 899-904
        • Strowig G.
        Making the switch to vendor-neutral archiving. How to optimize comprehensive data storage for healthcare's new age.
        Health Manag Technol. 2013; 34 (2-3): 10
        • Puppala M.
        • He T.
        • Chen S.
        • et al.
        METEOR: an enterprise health informatics environment to support evidence-based medicine.
        IEEE Trans Biomed Eng. 2015;
        • Starren J.B.
        • Winter A.Q.
        • Lloyd-Jones D.M.
        Enabling a learning health system through a unified enterprise data warehouse: the experience of the Northwestern University Clinical and Translational Sciences (NUCATS) Institute.
        Clin Transl Sci. 2015; 8: 269-271
        • Evans R.S.
        • Lloyd J.F.
        • Pierce L.A.
        Clinical use of an enterprise data warehouse.
        AMIA Annu Symp Proc. 2012; 2012: 189-198
        • Horvath M.M.
        • Winfield S.
        • Evans S.
        • et al.
        The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvement.
        J Biomed Inform. 2011; 44: 266-276
        • Chute C.G.
        • Beck S.A.
        • Fisk T.B.
        • et al.
        The enterprise data trust at Mayo Clinic: a semantically integrated warehouse of biomedical data.
        J Am Med Inform Assoc. 2010; 17: 131-135
        • Lacson R.
        • Khorasani R.
        Practical examples of natural language processing in radiology.
        J Am Coll Radiol. 2011; 8: 872-874
        • Channin D.S.
        • Mongkolwat P.
        • Kleper V.
        • et al.
        The annotation and image mark-up project.
        Radiology. 2009; 253: 590-592
        • Reiner B.I.
        Creating accountability in image quality analysis part 3: creation of a standardized image-centric mark-up and annotation tool.
        J Digit Imaging. 2013; 26: 600-604
        • Gorgolewski K.
        • Burns C.D.
        • Madison C.
        • et al.
        Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.
        Front Neuroinform. 2011; 5: 13
        • Zijdenbos A.P.
        • Forghani R.
        • Evans A.C.
        Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.
        IEEE Trans Med Imaging. 2002; 21: 1280-1291
        • Jee K.
        • Kim G.H.
        Potentiality of big data in the medical sector: focus on how to reshape the healthcare system.
        Healthc Inform Res. 2013; 19: 79-85
        • Mennes M.
        • Biswal B.B.
        • Castellanos F.X.
        • et al.
        Making data sharing work: the FCP/INDI experience.
        Neuroimage. 2013; 82: 683-691
        • Robson B.
        The dragon on the gold: myths and realities for data mining in biomedicine and biotechnology using digital and molecular libraries.
        J Proteome Res. 2004; 3: 1113-1119
        • Sox H.C.
        • Goodman S.N.
        The methods of comparative effectiveness research.
        Annu Rev Public Health. 2012; 33: 425-445
        • Grossmann C.
        • Powers B.
        • Sanders J.
        • et al.
        Issues and opportunities in the emergence of large health-related datasets.
        in: Digital data improvement priorities for continuous learning in health and health care: workshop summary. National Academies Press, Washington, DC2013: 27-32
        • Olsen L.
        • Grossmann C.
        • McGinnis J.M.
        • et al.
        Moving forward.
        in: Learning what works: infrastructure required for comparative effectiveness research: workshop summary. National Academies Press, Washington, DC2011: 315-332
        • Lord S.J.
        • Gebski V.J.
        • Keech A.C.
        Multiple analyses in clinical trials: sound science or data dredging?.
        Med J Aust. 2004; 181: 452-454