Informatics methods to enable sharing of quantitative imaging research data

Mia A Levy; John B Freymann; Justin S Kirby; Andriy Fedorov; Fiona M Fennessy; Steven A Eschrich; Anders E Berglund; David A Fenstermacher; Yongqiang Tan; Xiaotao Guo; Thomas L Casavant; Bartley J Brown; Terry A Braun; Andre Dekker; Erik Roelofs; James M Mountz; Fernando Boada; Charles Laymon; Matt Oborski; Daniel L Rubin

doi:10.1016/j.mri.2012.04.007

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Informatics methods to enable sharing of quantitative imaging research data

Journal article

Peer reviewed

Informatics methods to enable sharing of quantitative imaging research data

Mia A Levy, John B Freymann, Justin S Kirby, Andriy Fedorov, Fiona M Fennessy, Steven A Eschrich, Anders E Berglund, David A Fenstermacher, Yongqiang Tan, Xiaotao Guo, …

Magnetic resonance imaging, Vol.30(9), pp.1249-1256

11/2012

DOI: 10.1016/j.mri.2012.04.007

PMCID: PMC3466343

PMID: 22770688

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https://www.ncbi.nlm.nih.gov/pmc/articles/3466343View

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Abstract

The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.

Image meta-data repository

Clinical data repository

Quantitative Imaging Network

System architecture

Data sharing

Image repository

Research informatics

Imaging informatics

Details

Title: Subtitle: Informatics methods to enable sharing of quantitative imaging research data
Creators: Mia A Levy - Department of Biomedical Informatics and Medicine, Division of Hematology and Oncology, Vanderbilt University School of Medicine, Nashville, TN 37232-6838, USA
John B Freymann - Clinical Research Directorate/CMRP, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA
Justin S Kirby - Clinical Research Directorate/CMRP, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA
Andriy Fedorov - Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
Fiona M Fennessy - Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
Steven A Eschrich - H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
Anders E Berglund - H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
David A Fenstermacher - H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
Yongqiang Tan - Columbia University, New York, NY, USA
Xiaotao Guo - Columbia University, New York, NY, USA
Thomas L Casavant - Center for Bioinformatics and Computational Biology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
Bartley J Brown - Center for Bioinformatics and Computational Biology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
Terry A Braun - Center for Bioinformatics and Computational Biology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
Andre Dekker - Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
Erik Roelofs - Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
James M Mountz - University of Pittsburgh, Pittsburgh, PA, USA
Fernando Boada - University of Pittsburgh, Pittsburgh, PA, USA
Charles Laymon - University of Pittsburgh, Pittsburgh, PA, USA
Matt Oborski - University of Pittsburgh, Pittsburgh, PA, USA
Daniel L Rubin - Department of Radiology and Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, CA, USA
Resource Type: Journal article
Publication Details: Magnetic resonance imaging, Vol.30(9), pp.1249-1256
DOI: 10.1016/j.mri.2012.04.007
PMID: 22770688
PMCID: PMC3466343
NLM abbreviation: Magn Reson Imaging
ISSN: 0730-725X
eISSN: 1873-5894
Publisher: Elsevier Inc
Grant note: HHSN261200800001E / NCI 98411XSB2 / caBIG Imaging Workspace PAR-11-150 / NIH/NCI
Language: English
Date published: 11/2012
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering
Record Identifier: 9984064256702771

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