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The good news is that it is now possible to create large databases of information about images. The bad news is that each of these databases is protected by proprietary formats that do not communicate with one another. Researchers from each of the disciplines yielding these images refer to the images in a unique way, using different vocabulary. Wouldn't it be nice if a scientist could simply ask questions without regard to disciplinary boundaries and harness all of the available data about a tissue, cells, genes, proteins, and other parts of the body to prove or disprove a hypothesis?

Furthermore, animal research has also created a large volume of data. We know a lot about cancer from animals, have found that animals and humans respond much the same way to them, yet images of animals and images of humans are in completely different databases. Currently, there is no way to directly compare these image types because they do not share any of the same standards when it comes to metadata, or the description of the image.

All of these imaging disciplines have created mutually-exclusive yet rich data sets. The barriers created by proprietary data formats and a lack of common standards mean that the promise of integrating them awaits a technical solutions, technical solutions that comprise the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP).

CTIIP Sub-Projects

. It is now feasible to extract large sets of quantitative image features relevant to cancer prognosis or treatment across three complementary research domains: 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.

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