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To address these limitations, the CTIIP team is developing a unified query interface to make it easier to analyze data from different research domains. This interface, plus related open-source software and data standards, would then be applied to co-clinical, small animal model data, and provide a common platform and data engine for the hosting of “pilot challenges.” These pilot challenges will proactively facilitate biological and clinical research across the clinical, pre-clinical, and digital pathology imaging research domains.
The approach taken to development in this 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.
Most importantly, 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 following table presents the data that the CTIIP team is integrating through various means. This integration relies on the expansion of software features and on the application of data standards, as described in subsequent sections of this document.
| 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 sub-projects, along with the solutions they provide, are discussed in this guide and listed below.
is composed of the following sub-projects. Each project is discussed in this document.
| Sub-Project Name | Description |
|---|---|
| Digital Pathology and Integrated Query System | Address the interoperability of |
| Sub-Project Name | Solution it Provides |
| 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 | 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 pilot challenges would use limited data sets for proof-of-concept, and test the informatics infrastructure needed for such more rigorous “Grand Challenges” that would could later be scaled up and supported by extramural initiatives. |
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•Improvements to the EVS vocabularies
The following table presents the data that the CTIIP team is integrating through various means. This integration relies on the expansion of software features and on the application of data standards, as described in subsequent sections of this document.
| 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 |
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.
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