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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 clinical imaging, preco-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.
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Another challenge for CTIIP with its goal of integrating data from complimentary domains is the lack of a defined standard for preco-clinical and digital pathology data. Without a data standard for these domains, it is very difficult to share and leverage such data across studies and institutions. As part of the CTIIP project, the team will extend the DICOM model to co-clinical and small animal imaging.
NCI 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, Enterprise Vocabulary Services (EVS) and Cancer Data Standards Registry and Repository (caDSR) are harmonized with the Biomedical Research Integrated Domain Group (BRIDG), Study Data Tabulation Model (SDTM), and Health Level Seven® Reference Information Model HL7 RIM models. Standardized Case Report Forms (CRFs), including those for imaging, have also been created. The CBIIT project 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 and TCGA.
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Small Animal/Co-clinical Improved DICOM Compliance and Data Integration
While the challenges of integrating small animal/co-clinical data with data on humans are steep, the potential rewards are great. This goal depends on a common data standard and tool support. For example, consider the imperative to generate effective therapy for a cancer patient. Researchers could search in vivo imaging, radiology, and digital pathology data from the patient, run a gene panel, and identify abnormal genes in the patient. They could then compare these results to a genetic panel on a mouse, made possible with data described by the DICOM standard, to find a matching tumor. Small animal researchers could then experiment therapies on that mouse that could also benefit the human patient. Finally, the researchers could run an integrative query to develop a sophisticated diagnosis and treatment plan.
Directly comparing data from co-clinical animal models to real-time clinical data is the goal of
The impact on integrative research projects such as co-clinical trials would be to give researchers the ability to directly compare data from pre-clinical animal models with real-time clinical data.
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