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Informatics Methods to Enable Sharing of Quantitative Imaging Research Data

Institution:
1Department of Biomedical Informatics and Medicine, Division of Hematology and Oncology, Vanderbilt University School of Medicine, Nashville, TN, USA.
2Clinical Research Directorate/CMRP, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD, USA.
3Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
5Columbia University, New York, NY, USA.
6Center for Bioinformatics and Computational Biology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA.
7Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
8University of Pittsburgh, Pittsburgh, PA, USA.
9Department of Radiology and Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, CA, USA.
Publisher:
Elsevier
Publication Date:
Nov-2012
Journal:
Magnetic Resonance Imaging
Volume Number:
30
Issue Number:
9
Pages:
1249-56
Citation:
Magn Reson Imaging. 2012 Nov;30(9):1249-56.
Links:
http://dx.doi.org/10.1016/j.mri.2012.04.007
PubMed ID:
22770688
PMCID:
PMC3466343
Keywords:
Quantitative Imaging Network, data sharing, imaging informatics, research informatics, image repository, image meta-data repository, clinical data repository, system architecture
Appears in Collections:
Prostate Group, SLICER, SPL
Sponsors:
261200800001E/PHS HHS/United States
PAR-11-150/PHS HHS/United States
U01 CA142555/CA/NCI NIH HHS/United States
U01 CA151261/CA/NCI NIH HHS/United States
Generated Citation:
Levy M., Freymann J.B., Kirby J.S., Fedorov A., Fennessy F.M., Enschrich S.A., Berglund A.E., Fenstermacher D.A., Tan Y., Guo X., Casavant T.L., Brown B.J., Braun T.A., Dekker A., Roelofs E., Mountz J.M., Boada F., Laymon C., Oborski M., Rubin D.L. Informatics Methods to Enable Sharing of Quantitative Imaging Research Data. Magn Reson Imaging. 2012 Nov;30(9):1249-56. PMID: 22770688. PMCID: PMC3466343.
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Introduction:: 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. Methods:: 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. Results:: 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. Conclusions:: 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.

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