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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. Radiologists and pathologists represent different scientific disciplines, however, without a common vocabulary. 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 or a geneticist 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. Like radiology and pathology, genomics uses its own vocabulary, preventing data from being directly shared.

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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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 are the sub-projects of the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP).

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