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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 the tumor. To gather even more information, a doctor may run a genetic test and determine that the patient has a genetic anomaly. This genetic anomaly may be something that researchers have already matched, or may match in the future, with an effective therapy, thanks to recent advances in precision medicine.

Yet another way we learn about cancer in humans is through small animal research. Images from small animals allow detailed study of biological processes, disease progression, and response to therapy, with the potential to provide a natural bridge to human disease. Due to differences in how data is collected and stored about animals and humans, however, the bridge is man-made.

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 specialized knowledge to handle and interpret them. While complementary, each specialist comes from a different scientific discipline.

 If If you were the patient, wouldn't you want your medical team to benefit from all of the latest advances in precision medicine, whether those advances come from clinical imaging, co-clinical imaging (small animal research), or digital pathologydata collected about your cancer, no matter which discipline it belongs to?

The good news is that it is now possible to create large databases of information about images and 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 yielding these images under an 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?

All of these imaging disciplines have created mutually-exclusive yet essential data sets. The barriers between them, which are created by proprietary data formats and a lack of shared standards, mean that the Big Data. The promise of integrating them awaits technical solutions. A significant start to these technical solutions comprise the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) and its sub-projects.

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