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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 of in vivo clinical imaging, pre-clinical imaging, and molecular 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 does not accept is incapable of integrating data from other research domains (and/or is not standards-based?)due to a lack of common standards.
To address these limitations, the CTIIP team will develop 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 clinical/co-clinical settings ( small animal models)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 three NCI divisionsthe 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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•Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Integrative Query System
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Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
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•Identify co-clinical pilot data set and populate integrated ‘omics/imaging infrastructure.
Challenges
Solutions
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Challenges
Solutions
Pilot Challenges
1) AIM 3 - “Pilot Challenges” to compare the decision support systems for three imaging research domains: Clinical Imaging, Pre-clinical Imaging, and Digital Pathology.
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Once participants upload their results, they can see them in ePad.
Challenges
Solutions
Scenarios
Need to generate proper therapy for a patient. Look at in vivo imaging, radiology and pathology, run a gene panel to look for abnormal. Look at co-clinical trials (model of a tumor in a mouse that is similar to a human. Experiment therapies on mice.) Run an integrative query to develop a sophisticated diagnosis. Search big data.
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