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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 have a precise match to 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.

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 If you were the patient, wouldn't you want your medical team to benefit from an integration of all of these imagesall of the latest advances in precision medicine, whether those advances come from clinical imaging, co-clinical imaging (small animal research), or digital pathology?

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?

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