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Imaging-based cancer research is in the beginning phase of an integrative-biology revolution. It is now feasible to extract large sets of quantitative image features relevant to cancer prognosis or treatment across three complementary research domains: in vivo clinical imaging, pre-clinical imaging, and digital pathology. These high-dimensional image feature sets can be used to infer clinical phenotypes or correlate with gene–protein signatures. This type of analysis, however, requires large volumes of data.

To serve the need for research across domains, the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) team is creating a set of open-source software tools that support a comprehensive and reusable exploration and fusion of in vivo of clinical imaging, pre-clinical imaging, and digital pathology data. The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) projects, with molecular metadata and image-derived information, respectively, have created a rich multi-domain data set. This data set, however, is in an infrastructure that provides limited query capability for identifying cases based on all of the available data types. Moreover, this infrastructure is incapable of integrating data from other research domains due to a lack of common data standards.

To address these limitations, the CTIIP team will develop is developing a unified query interface to make it easier to analyze data from different research domains. This interface, plus related open-source software and data standards, would then be applied to small animal model data, and provide a common platform and data engine for the hosting of “pilot challenges.” These pilot challenges will proactively facilitate biological and clinical research across the clinical imaging, pre-clinical imaging, and digital pathology imaging research domains. The algorithms used in the pilot challenges will be shared with the community via an open-source software clearinghouse.

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