Reinventing Radiology: Big Data and the Future of Medical Imaging

Purpose: Today, data surrounding most of our lives are collected and stored. Data scientists are beginning to explore applications that could harness this information and make sense of it. Materials and Methods: In this review, the topic of Big Data is explored, and applications in modern health care are considered. Results: Big Data is a concept that has evolved from the modern trend of “scientism.” One of the primary goals of data scientists is to develop ways to discover new knowledge from the vast quantities of increasingly available information. Conclusions: Current and future opportunities and challenges with respect to radiology are provided with emphasis on cardiothoracic imaging.

[1]  Dorothy M Adcock,et al.  Prospective validation of Wells Criteria in the evaluation of patients with suspected pulmonary embolism. , 2004, Annals of emergency medicine.

[2]  Stella K Kang,et al.  Residents' Introduction to Comparative Effectiveness Research and Big Data Analytics. , 2017, Journal of the American College of Radiology : JACR.

[3]  XuZeshui,et al.  Towards felicitous decision making , 2016 .

[4]  Daniel L. Rubin,et al.  The caBIG™ Annotation and Image Markup Project , 2009, Journal of Digital Imaging.

[5]  S. Campbell History of Ultrasound in Obstetrics and Gynecology , 2006 .

[6]  J. Salmond,et al.  Can big data tame a “naughty” world? , 2017 .

[7]  Sushil Jajodia,et al.  SHARE , 2018, ACM Transactions on Internet Technology.

[8]  D M D'Alessandro,et al.  The virtual hospital: the digital library moves from dream to reality. , 1999, Academic radiology.

[9]  B. Kramer,et al.  The National Lung Screening Trial: Results stratified by demographics, smoking history, and lung cancer histology , 2013, Cancer.

[10]  M P D'Alessandro,et al.  Distributing an electronic thoracic imaging teaching file using the Internet, Mosaic, and personal computers. , 1995, AJR. American journal of roentgenology.

[11]  Sebastian Flacke,et al.  Performance of ACR Lung-RADS in a Clinical CT Lung Screening Program. , 2016, Journal of the American College of Radiology : JACR.

[12]  J. Austin,et al.  Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. , 2002, Radiology.

[13]  David A. Clunie,et al.  DICOM Structured Reporting , 2000 .

[14]  V. Moyer Screening for Lung Cancer: U.S. Preventive Services Task Force Recommendation Statement , 2014, Annals of Internal Medicine.

[15]  Stuart N. Lane,et al.  Knowledge-theoretic models in hydrology , 2010 .

[16]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[17]  M P D'Alessandro,et al.  The virtual hospital: a new paradigm for lifelong learning in radiology. , 1994, Radiographics : a review publication of the Radiological Society of North America, Inc.

[18]  J. Locke An Essay concerning Human Understanding , 1924, Nature.

[19]  Michael P. D'Alessandro,et al.  The virtual naval hospital: the digital library as knowledge management tool for nomadic patrons , 2001, JCDL '01.

[20]  William Seffens,et al.  Machine Learning Data Imputation and Classification in a Multicohort Hypertension Clinical Study , 2015, Bioinformatics and biology insights.

[21]  E. Siegel,et al.  Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment , 2013, Current Cardiology Reports.

[22]  Luciano Floridi,et al.  The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts , 2015, Science and Engineering Ethics.

[23]  Anne C. Beal,et al.  The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. , 2012, Journal of the American Medical Association (JAMA).

[24]  Eliot L. Siegel,et al.  Data-Driven Decision Support for Radiologists: Re-using the National Lung Screening Trial Dataset for Pulmonary Nodule Management , 2014, Journal of Digital Imaging.

[25]  A. Arbab-Zadeh Stress testing and non-invasive coronary angiography in patients with suspected coronary artery disease: time for a new paradigm , 2012, Heart international.

[26]  A. A. Long,et al.  The Hellenistic Philosophers , 1989, The Classical Review.

[27]  Ronald Epstein,et al.  Time and the patient-physician relationship , 1999, Journal of General Internal Medicine.

[28]  G. Galilei Dialogue Concerning the Two Chief World Systems, Ptolemaic and Copernican , 1953 .

[29]  Alistair A. Young,et al.  The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart , 2011, Bioinform..

[30]  J. Austin,et al.  Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. , 2005, Radiology.

[31]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[32]  Vincent Liu,et al.  Automated identification of pneumonia in chest radiograph reports in critically ill patients , 2013, BMC Medical Informatics and Decision Making.

[33]  Nancy Knight,et al.  Radiology reporting, past, present, and future: the radiologist's perspective. , 2007, Journal of the American College of Radiology : JACR.

[34]  D M D'Alessandro,et al.  The Virtual Hospital: an IAIMS integrating continuing education into the work flow. , 1996, M.D. computing : computers in medical practice.

[35]  F. Harrell,et al.  Prognostic value of a treadmill exercise score in outpatients with suspected coronary artery disease. , 1991, The New England journal of medicine.

[36]  D. Aberle,et al.  CT screening for lung cancer. , 2007, The New England journal of medicine.

[37]  Andrea De Mauro,et al.  A formal definition of Big Data based on its essential features , 2016 .

[38]  John P. A. Ioannidis,et al.  Big data meets public health , 2014, Science.

[39]  Richard Platt,et al.  Big data in epidemiology: too big to fail? , 2013, Epidemiology.

[40]  Frank Pasquale,et al.  The Spectrum of Control: A Social Theory of the Smart City , 2015, First Monday.

[41]  M. L. R. D. Christenson,et al.  Guidelines for Management of Small Pulmonary Nodules Detected on CT Scans: A Statement From the Fleischner Society , 2006 .

[42]  F. Harrell,et al.  Exercise treadmill score for predicting prognosis in coronary artery disease. , 1987, Annals of internal medicine.

[43]  Richard D. White,et al.  ACR Appropriateness Criteria® Chronic Chest Pain-High Probability of Coronary Artery Disease. , 2017, Journal of the American College of Radiology : JACR.

[44]  Ahmedin Jemal,et al.  Annual number of lung cancer deaths potentially avertable by screening in the United States , 2013, Cancer.

[45]  B. Rhoads,et al.  The role and character of theory in geomorphology , 2011 .

[46]  M. Pencina,et al.  General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study , 2008, Circulation.

[47]  Gregory Piatetsky-Shapiro,et al.  High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality , 2000 .

[48]  Rajkumar Buyya,et al.  The anatomy of big data computing , 2015, Softw. Pract. Exp..

[49]  Jake Luo,et al.  Big Data Application in Biomedical Research and Health Care: A Literature Review , 2016, Biomedical informatics insights.

[50]  R. Detrano,et al.  Quantification of coronary artery calcium using ultrafast computed tomography. , 1990, Journal of the American College of Cardiology.

[51]  M P D'Alessandro,et al.  The virtual hospital: a link between academia and practitioners. , 1994, Academic medicine : journal of the Association of American Medical Colleges.

[52]  Charles Anderson,et al.  The end of theory: The data deluge makes the scientific method obsolete , 2008 .

[53]  Pattanasak Mongkolwat,et al.  Informatics in radiology: An open-source and open-access cancer biomedical informatics grid annotation and image markup template builder. , 2012, Radiographics : a review publication of the Radiological Society of North America, Inc.

[54]  Jasmin A. Tiro,et al.  Population-Based Precision Cancer Screening: A Symposium on Evidence, Epidemiology, and Next Steps , 2016, Cancer Epidemiology, Biomarkers & Prevention.

[55]  Lee M. Christensen,et al.  Natural Language Processing to identify pneumonia from radiology reports , 2013, Pharmacoepidemiology and drug safety.

[56]  M. P. D'Alessandro,et al.  The virtual hospital: the future of information distribution in medicine , 1993, SIGB.

[57]  Kwang-Pyo Kim,et al.  Myocardial Perfusion Scans: Projected Population Cancer Risks From Current Levels of Use in the United States , 2010, Circulation.

[58]  Udo Hoffmann,et al.  ACR Appropriateness Criteria Acute Nonspecific Chest Pain-Low Probability of Coronary Artery Disease. , 2012, Journal of the American College of Radiology : JACR.

[59]  Zeshui Xu,et al.  Towards felicitous decision making: An overview on challenges and trends of Big Data , 2016, Inf. Sci..

[60]  G. Diamond,et al.  Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. , 1979, The New England journal of medicine.

[61]  Eliot L. Siegel,et al.  Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset , 2017, J. Am. Medical Informatics Assoc..

[62]  M P D'Alessandro,et al.  Hand-held digital books in radiology: convenient access to information. , 1995, AJR. American journal of roentgenology.

[63]  Peter L. Elkin,et al.  Detection of pneumonia using free-text radiology reports in the BioSense system , 2011, Int. J. Medical Informatics.

[64]  Eliot L. Siegel,et al.  Computer-Aided Reporting of Chest Radiographs: Efficient and Effective Screening in the Value-Based Imaging Era , 2017, Journal of Digital Imaging.

[65]  G. Lakoff,et al.  Metaphors We Live by , 1981 .

[66]  Kirsten E. Martin Ethical Issues in the Big Data Industry , 2015, MIS Q. Executive.