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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 developing and deploying a set of open source software tools that supports a comprehensive and reusable exploration and fusion of imaging, clinical, and molecular data. The The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) projects 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. (For example...?) The CTIIP team will therefore develop a unified query interface to facilitate cross-disciplinary analysis. This infrastructure would then be applied to clinical/co-clinical settings and provide a common platform and data engine for the hosting of “pilot challenges.” .” These pilot challenge projects will proactively facilitate biological and clinical research across three NCI divisions. The algorithms used in the pilot challenges will be shared with the community via an open-source software clearinghouse.
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Data Standards Applicable to CTIIP
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.
How do we better treat our patients?
The result will be a set of open source software tools that allow researchers to create queries combining attributes from molecular, imaging, and clinical data, and to use such integrated queries to explore, filter, and select data for their driving biological problems. 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. The pilot challenge projects will proactively facilitate biological and clinical research across three NCI divisions. This is highly consistent with the research goals of the Informatics Imaging Working Group, the needs raised at the Imaging Informatics Workshop in March 2013, and the mission of CBIIT, and leverages critical resources and previous NCI investments to target important cancer problems, such as clinical decision support for predicting or assessment of response to therapy. All of these goals are consistent with the NCI BSA recommendations for CBIIT and the NCI focus on precision medicine. The approach taken to development in this project emphasizes modular semantic interoperability and open source tooling, making it immediately valuable to scientists with NCI funded research networks in the three research domains, as well as the national and international research communities, and providing a framework for enhanced adoption of these methods by biologists in the larger genomics/proteomic communities.
Three separate sections with problem/solution for each aim. Status of the solution.
Informatics have to let us communicate. Need to be able to compare the data between the omics.
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.
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.
Digital Pathology and Integrated Query System
Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. While DICOM has published a digital pathology standard, the major vendors in this space have not widely adopted the standard.
This common infrastructure depends on data interoperability, which requires adherence to data standards. Data standards ensure that While standards such as DICOM and AIM exist, vendors of data viewers and other tools required for data analysis have not widely adopted them.
The result of this lack of uniformly accepted standards is that outside a given laboratory of small collaborative groups, the integration of pathology data with radiographic, genomic, and proteomic data is all but impossible.
Furthermore, because each pathology-imaging vendor produces its own image management systems, these systems are also, by extension, proprietary and not standardized. The result is that images produced on different systems cannot be analyzed via the same mechanisms.
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The result will be that allow researchers to create queries combining attributes from molecular, imaging, and clinical data, and to use such integrated queries to explore, filter, and select data for their driving biological problems.
Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. While DICOM has published a digital pathology standard, the major vendors in this space have not widely adopted the standard.
This common infrastructure depends on data interoperability, which requires adherence to data standards. Data standards ensure that While standards such as DICOM and AIM exist, vendors of data viewers and other tools required for data analysis have not widely adopted them.
The result of this lack of uniformly accepted standards is that outside a given laboratory of small collaborative groups, the integration of pathology data with radiographic, genomic, and proteomic data is all but impossible.
Furthermore, because each pathology-imaging vendor produces its own image management systems, these systems are also, by extension, proprietary and not standardized. The result is that images produced on different systems cannot be analyzed via the same mechanisms.
In addition, no standard currently exists for markup and annotations on images.
Each domain is at a different step in maturity. proprietary data formats
- The lack of standards in pre-clinical and pathology prevents the ability to share and leverage data across studies and institutions.
- There are differences between the domains, and therefore there should be careful consideration of where there are commonalities in semantic interoperability, and where there is not.
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.
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.
How do we better treat our patients?
This is highly consistent with the research goals of the Informatics Imaging Working Group, the needs raised at the Imaging Informatics Workshop in March 2013, and the mission of CBIIT, and leverages critical resources and previous NCI investments to target important cancer problems, such as clinical decision support for predicting or assessment of response to therapy. All of these goals are consistent with the NCI BSA recommendations for CBIIT and the NCI focus on precision medicine. The approach taken to development in this project emphasizes modular semantic interoperability and open source tooling, making it immediately valuable to scientists with NCI funded research networks in the three research domains, as well as the national and international research communities, and providing a framework for enhanced adoption of these methods by biologists in the larger genomics/proteomic communities.
Three separate sections with problem/solution for each aim. Status of the solution.
Informatics have to let us communicate. Need to be able to compare the data between the omics.
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.
Digital Pathology and Integrated Query System
This sub-project addresses the lack of uniformly accepted standards within digital pathology and the simultaneous need for integration of pathology data with radiographic, genomic, and proteomic data.
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iii) Standards-based image annotation utilizing the Annotation Image Markup (AIM) standard.
b) Integrative Queries
i) Programmatic Access to Data to TCGA-related image data.
ii) Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Standards
Each domain is at a different step in maturity. proprietary data formats
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) Integrative Queries
i) Programmatic Access to Data to TCGA-related image data.
ii) Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Integrative Query System
Look at Ulli's PPT
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Small Animal/Co-clinical Improved DICOM Compliance and Data Integration
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.
Developing DICOM standards for small animal imaging and identify co-clinical datasets to test the integration of TCIA and TCGA for this data.
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