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Each of these types of information is an image of a different scale image, from a different scientific discipline. A large-scale image like an X-ray may be almost life-size. Genes, on the other hand, fit on a slide that is put under a microscope. It may be that an animal has had the same exact type of tumor the patient has. If you were the patient, wouldn't you want your medical team to benefit from an integration of all of these images?

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. In fact, each database is associated with a distinct scientific discipline, without the expectation that they could be integratedResearchers 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 that data 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. It is not that the data do not exist, it is that the technical solutions for integrating them do not exist yet. .

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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