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Most cancer diagnoses are made based on images. You have to see a tumor, or compare images of it over time, to determine its level of threat. Ultrasounds, MRIs, and X-rays are all common types of images that radiologists use to collect information about a patient and perhaps cause a doctor to recommend a biopsy. Once that section of the tumor is under the microscope, pathologists learn more about it. To gather even more information, a doctor may order a genetic panel. If that panel shows that the patient has a genetic anomaly, the doctor may search for clinical trials that match it, or turn to therapies that researchers have already proven effective for this combination of tumor and genetic anomaly through recent advances in precision medicine.
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Each of these diagnostic images are at a different scale, from a different scientific discipline. A large-scale image like an X-ray may be almost life-size. Slices of tumors are smaller still. Like genes and proteins, you must put them on a slide under a microscope to see them. Not surprisingly, each of these scales requires image types require specialized knowledge to create, handle, and interpret them. While complementary, each specialist comes from a different scientific discipline.
If you were the patient, wouldn't you want your medical team to benefit from data collected about your cancer, no matter which discipline it belongs belonged to?
The good news is that it is now possible to both create large databases of information about images and apply existing data standards do exist. The bad news is that each of these databases is protected by proprietary formats that do not communicate with one another, and standards do not yet exist for all image types. Researchers from each of the disciplines under an the umbrella term called imaging 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?
One promise of big data, such as that represented by the large but mutually-exclusive imaging data sets mentioned so far, is that mashups can be made that integrate two or more data sets in a single graphical interface so that doctors, pathologists, radiologists, and laboratory technicians can make connections that improve outcomes for patients. Such mashups require and await technical solutions in the areas of data standards and software development. A significant start to all of these technical solutions comprise are the sub-projects of the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) and its sub-projects.
CTIIP Sub-Projects
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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