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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 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 image data. In this project, we propose to develop and deploy software that supports a comprehensive and reusable exploration and fusion of imaging, clinical, and molecular data. Within these three research domains, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. For pre-clinical imaging and digital pathology, there are no such standards that allow for the seamless viewing, integration, and analysis of disparate data sets to produce integrated views of the data, quantitative analysis, data integration, and research or clinical decision support systems.
This research theme has been systematically explored by the NCI Imaging Informatics Working Group since earlier this year, as a joint effort by NCI extramural staff and CBIIT staff. In addition, these informatics needs were well articulated at the Imaging Informatics Working Group Workshop held on March 5th, 2013 that followed a and April 14th, 2015 that were in conjunction with the Quantitative Imaging Network (QIN) annual meetingmeetings. At this workshopthese workshops, QIN members leveraged their experience to explore novel informatics solutions for pre-clinical imaging, co-clinical trials, and digital pathology, and validated the needs met by the projects described in this document.
Some of the relevant, high-level takeaways from the workshop can be summarized as follows:
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The overarching goal of this project is to establish an informatics infrastructure that demonstrates the benefit and feasibility of data interoperability across the three domains: Genomics, Diagnostic Imaging, and Digital Pathology. The intent is to identify and address the interoperability needs to support specific research objectives, with the goal of demonstrating the need to scale up. The scope is limited to pilot data sets, and the intent is only to demonstrate the infrastructure. Creation of more robust tools that leverage the interoperability and infrastructure created in this project would be supported through extramural support after the benefit of scaling up has been demonstrated.
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CBIIT has worked extensively for several years in the area of data standards for both clinical research and healthcare, working with the community and Standards Development Organizations (SDOs), such as the Clinical Data Interchange Standards Consortium (CDISC), Health Level 7 (HL7) and the International Organization for Standardization (ISO). From that work, EVS and caDSR is harmonized with the BRIDG, SDTM, and HL7 RIM models. Standardized Case Report Forms (CRFs), including those for imaging, have also been created. This work provides the bioinformatics foundation for semantic interoperability in digital pathology and co-clinical trials integrated with clinical and patient demographic data and data contained in TCIA / TCGA.
Installation and Downloads
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Support
What's New
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Presentations, Demos and Other Materials
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- Link to other outreach materials (presentations, papers)Coming soon
Documentation and Training
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Related Applications
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