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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 cancer 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) /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 available. . (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”. The algorithms used in the pilot challenges will be shared with the community via an open-source software clearinghouse.
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 will be available for cross-domain analysis using tools developed by the 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 CTIIP project will also address the creation of a standard for image annotations and markup.
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The sub-projects, along with the solutions they provide, are discussed in this guide and listed below.
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