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• Digital Pathology and Integrated Query System
• Small Animal/Co-clinical Improved DICOM Compliance and Data Integration
• DICOM Working Group 30
• Pilot Challenges

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, 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. 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.

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.

, 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 solutionImaging-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.

Informatics have to let us communicate. Need to be able to compare the data between the omics.

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