Quantitative imaging network: Data sharing and competitive algorithm validation leveraging the cancer imaging archive

Jayashree Kalpathy-Cramer, John Blake Freymann, Justin Stephen Kirby, Paul Eugene Kinahan, And Fred William Prior

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)147-152
Number of pages6
JournalTranslational Oncology
Issue number1
StatePublished - Feb 2014
Externally publishedYes

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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