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The National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) unites sub-projects from several imaging domains with the goal of creating a common informatics infrastructure and open-source software tools. The purpose of this infrastructure and tools is to make it possible to run integrative queries and answer research questions using data from domains that have not previously been comparable. The following table presents the data associated with each domain addressed by the CTIIP project.
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-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 data.
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 software that supports a comprehensive and reusable exploration and fusion of imaging, clinical, and molecular data. The Cancer Genome Atlas (TCGA)/
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The Cancer Imaging Archive (TCIA)
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Each domain also has associated image annotations and markup that must adhere to a standard to enable integrative queries.
At the conclusion of this project, the common informatics infrastructure will provide researchers with analysis tools they can use to directly mine data from multiple high-volume information repositories, creating a foundation for research and decision support systems to better diagnose and treat patients with cancer.
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 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. TCGA/TCIA provides a rich multi-domain dataset in an infrastructure that provides limited query capability for identifying cases based on all the data types available. Cross-disciplinary analysis would be facilitated by providing a unified query interface. This infrastructure would then be applied to clinical -co-clinical settings and provide a common platform and data engine for hosting of “pilot challenges”. An opensource software clearinghouse will enable community sharing of algorithms used in the analyses.
The sub-projects, along with the solutions they provide, are discussed in this guide and listed below.
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.
Three complementary projects were proposed and approved.
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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 the data types available. 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 hosting of “pilot challenges”. The algorithms used in the pilot challenges will be shared with the community via an open-source software clearinghouse.
The following table presents the data that will be available for cross-domain analysis using tools developed by the CTIIP team.
| Domain | Data Set |
|---|---|
| Clinical Imaging | The Cancer Genome Atlas (TCGA) clinical and molecular data |
| The Cancer Imaging Archive (TCIA) in-vivo imaging data | |
| Pre-clinical | Small animal models |
| Digital Pathology | caMicroscope |
The CTIIP project will also address the creation of a standard for image annotations and markup.
At the conclusion of this project, the common informatics infrastructure will provide researchers with analysis tools they can use to directly mine data from multiple high-volume information repositories, creating a foundation for research and decision support systems to better diagnose and treat patients with cancer.
The sub-projects, along with the solutions they provide, are discussed in this guide and listed below.
| Sub-Project | Goal |
|---|---|
| Digital Pathology and Integrated Query System | Address the interoperability of digital pathology data, improve integration and analytic capabilities between TCIA and TCGA, and raise the level of interoperability to create the foundation required for pilot demonstration projects in each of the targeted research domains: clinical imaging, pre-clinical imaging, and digital pathology imaging. |
| DICOM Standards for Small Animal Imaging; Use of Informatics for Co-clinical Trials | Address the need for standards in pre-clinical imaging and test the informatics created in the Digital Pathology and Integrated Query System sub-project for decision support in co-clinical trials. |
| Pilot Challenges | Leverage the work done in the Digital Pathology and Integrated Query System sub-project to further enhance the informatics and infrastructure in several Pilot Challenges. These challenges will be designed to develop knowledge extraction tools and compare decision support systems for the three research domains, which will now be represented as a set of integrated data from TCIA and TCGA. The intent is not to specifically implement a rigorous “Grand Challenge”, but rather to develop Pilot Challenge projects.These would use limited data sets for proof-of-concept, and test the informatics infrastructure needed for such “Grand Challenges” that would be scaled up and supported by extramural initiatives later in 2014 and beyond. |
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.
Three complementary projects were proposed and approved.
...
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