Literature DB >> 26683510

Big Data and the Future of Radiology Informatics.

Akash P Kansagra1, John-Paul J Yu2, Arindam R Chatterjee3, Leon Lenchik4, Daniel S Chow5, Adam B Prater6, Jean Yeh2, Ankur M Doshi7, C Matthew Hawkins6, Marta E Heilbrun8, Stacy E Smith9, Martin Oselkin10, Pushpender Gupta4, Sayed Ali11.   

Abstract

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.
Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Keywords:  Radiology; informatics; personalized medicine; value; workflow

Mesh:

Year:  2015        PMID: 26683510     DOI: 10.1016/j.acra.2015.10.004

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  13 in total

1.  Identifying patients with neuronal intranuclear inclusion disease in Singapore using characteristic diffusion-weighted MR images.

Authors:  Wai-Yung Yu; Zheyu Xu; Hwei-Yee Lee; Aya Tokumaru; Jeanne M M Tan; Adeline Ng; Shigeo Murayama; C C Tchoyoson Lim
Journal:  Neuroradiology       Date:  2019-07-11       Impact factor: 2.804

2.  Imaging Informatics: 25 Years of Progress.

Authors:  J P Agrawal; B J Erickson; C E Kahn
Journal:  Yearb Med Inform       Date:  2016-06-30

Review 3.  [Big data in imaging].

Authors:  Philipp Sewerin; Benedikt Ostendorf; Axel J Hueber; Arnd Kleyer
Journal:  Z Rheumatol       Date:  2018-04       Impact factor: 1.372

4.  Big data in oncologic imaging.

Authors:  Daniele Regge; Simone Mazzetti; Valentina Giannini; Christian Bracco; Michele Stasi
Journal:  Radiol Med       Date:  2016-09-13       Impact factor: 3.469

5.  Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes.

Authors:  Jerome Jourquin; Stephanie Birkey Reffey; Cheryl Jernigan; Mia Levy; Glendon Zinser; Kimberly Sabelko; Jennifer Pietenpol; George Sledge
Journal:  JCO Precis Oncol       Date:  2019-09-12

6.  A peek into the future of radiology using big data applications.

Authors:  Amit T Kharat; Shubham Singhal
Journal:  Indian J Radiol Imaging       Date:  2017 Apr-Jun

7.  Trends in radiology and experimental research.

Authors:  Francesco Sardanelli
Journal:  Eur Radiol Exp       Date:  2017-06-29

8.  Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain.

Authors:  John Ford; Nesrin Dogan; Lori Young; Fei Yang
Journal:  Contrast Media Mol Imaging       Date:  2018-07-30       Impact factor: 3.161

Review 9.  The Challenges of Diagnostic Imaging in the Era of Big Data.

Authors:  Marco Aiello; Carlo Cavaliere; Antonio D'Albore; Marco Salvatore
Journal:  J Clin Med       Date:  2019-03-06       Impact factor: 4.241

Review 10.  Considerations for ethics review of big data health research: A scoping review.

Authors:  Marcello Ienca; Agata Ferretti; Samia Hurst; Milo Puhan; Christian Lovis; Effy Vayena
Journal:  PLoS One       Date:  2018-10-11       Impact factor: 3.240

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