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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 test, which panel. If that panel shows 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 , the doctor may search for clinical trials that match it, or turn to therapies that researchers have proven effective for this combination of tumor and genetic anomaly through recent advances in precision medicine.
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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 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?
The One promise of Big Databig data, such as that represented by the large but mutually-exclusive imaging data sets in each disciplinementioned 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 standard development standards and implementation, software development, and innovative applications of the resulting integration. A significant start to all of these technical solutions comprise the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) and its sub-projects.
CTIIP Sub-Projects
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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.
To address these limitations, the CTIIP team is developing a unified query interface to make it easier to analyze data from different research domains. This interface, plus related open-source software and data standards, would then be applied to co-clinical, small animal model data, and provide a common platform and data engine for the hosting of “pilot challenges.” These pilot challenges will proactively facilitate biological and clinical research across the clinical, pre-clinical, and digital pathology imaging research domains.
This project emphasizes modular semantic interoperability and open source tooling, making it immediately valuable to scientists in the national and international research communities, and providing a framework for enhanced adoption of these methods by biologists in the larger genomics/proteomic communities.
Most importantly, the The common informatics infrastructure will provide researchers with analysis tools they can use to directly mine data from multiple high-volume information repositories, creating a foundation for research and decision support systems to better diagnose and treat patients with cancer.
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