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Each of these types of information is a different scale image. 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. If you were the patient, wouldn't you want your medical team to benefit from an integration of all of these images?
For example, breast cancer has biomarkers (progesterone status, etc.). One question to ask is "if the estrogen status is negative in humans, what does the pathology look like?" Then compare this to mice. Is the model we have a good model for the human condition?
Slides of cells, however, also tell us something about
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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. In fact, each database is associated with a distinct scientific discipline, without the expectation that they could be integrated. Wouldn't it be nice if a scientist could ask questions without regard to disciplinary boundaries and harness all of that data 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.
. 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.
To serve the need for research across domains, the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) team is creating a set of open-source software tools that support a comprehensive and reusable exploration and fusion of clinical imaging, co-clinical imaging, and digital pathology data. The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) projects, with molecular metadata and image-derived information, respectively, 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. Moreover, this infrastructure is incapable of integrating data from other research domains due to a lack of common data standards.
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Informatics help us communicate. It can help us better treat our patients.
For example, breast cancer has biomarkers (progesterone status, etc.). One question to ask is "if the estrogen status is negative in humans, what does the pathology look like?" Then compare this to mice. Is the model we have a good model for the human condition?
