TY - JOUR
T1 - Quantitative imaging network
T2 - Data sharing and competitive algorithm validation leveraging the cancer imaging archive
AU - Kalpathy-Cramer, Jayashree
AU - Freymann, John Blake
AU - Kirby, Justin Stephen
AU - Kinahan, Paul Eugene
AU - Prior, And Fred William
N1 - Funding Information:
Address all correspondence to: Jayashree Kalpathy-Cramer, PhD, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 13th St, Charlestown, MA 01940. E-mail: kalpathy@nmr.mgh.harvard.edu 1This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health (NIH), under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. J.K.-C. is funded in part by the NIH grants U01CA154602 and R00LM009889 and a contract ST13-4130. P.E.K. is funded in part by the NIH grant U01CA148131 and Contract 24XS036-004. Received 16 December 2013; Revised 17 March 2014; Accepted 19 March 2014 Copyright © 2014 Neoplasia Press, Inc. All rights reserved 1944-7124/14/$25.00 DOI 10.1593/tlo.13862
PY - 2014/2
Y1 - 2014/2
N2 - The Quantitative Imaging Network (QIN), supported by the National Cancer Institute, is designed to promote research and development of quantitative imaging methods and candidate biomarkers for the measurement of tumor response in clinical trial settings. An integral aspect of the QIN mission is to facilitate collaborative activities that seek to develop best practices for the analysis of cancer imaging data. The QIN working groups and teams are developing new algorithms for image analysis and novel biomarkers for the assessment of response to therapy. To validate these algorithms and biomarkers and translate them into clinical practice, algorithms need to be compared and evaluated on large and diverse data sets. Analysis competitions, or "challenges," are being conducted within the QIN as a means to accomplish this goal. The QIN has demonstrated, through its leveraging of The Cancer Imaging Archive (TCIA), that data sharing of clinical images across multiple sites is feasible and that it can enable and support these challenges. In addition to Digital Imaging and Communications in Medicine (DICOM) imaging data, many TCIA collections provide linked clinical, pathology, and "ground truth" data generated by readers that could be used for further challenges. The TCIA-QIN partnership is a successful model that provides resources for multisite sharing of clinical imaging data and the implementation of challenges to support algorithm and biomarker validation.
AB - The Quantitative Imaging Network (QIN), supported by the National Cancer Institute, is designed to promote research and development of quantitative imaging methods and candidate biomarkers for the measurement of tumor response in clinical trial settings. An integral aspect of the QIN mission is to facilitate collaborative activities that seek to develop best practices for the analysis of cancer imaging data. The QIN working groups and teams are developing new algorithms for image analysis and novel biomarkers for the assessment of response to therapy. To validate these algorithms and biomarkers and translate them into clinical practice, algorithms need to be compared and evaluated on large and diverse data sets. Analysis competitions, or "challenges," are being conducted within the QIN as a means to accomplish this goal. The QIN has demonstrated, through its leveraging of The Cancer Imaging Archive (TCIA), that data sharing of clinical images across multiple sites is feasible and that it can enable and support these challenges. In addition to Digital Imaging and Communications in Medicine (DICOM) imaging data, many TCIA collections provide linked clinical, pathology, and "ground truth" data generated by readers that could be used for further challenges. The TCIA-QIN partnership is a successful model that provides resources for multisite sharing of clinical imaging data and the implementation of challenges to support algorithm and biomarker validation.
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U2 - 10.1593/tlo.13862
DO - 10.1593/tlo.13862
M3 - Article
AN - SCOPUS:84902513849
SN - 1944-7124
VL - 7
SP - 147
EP - 152
JO - Translational Oncology
JF - Translational Oncology
IS - 1
ER -