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Introduction to CTIIP

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. 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.

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One promise of big data is that data mashups can integrate two or more data sets in a single 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).

CTIIP Sub-Projects

As discussed so far, cancer research is needed across disciplines. To serve this need, the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) team plans to meet it by creating a data mashup interface, along with other software and standards, that accesses The Cancer Genome Atlas (TCGA) clinical and molecular data, The Cancer Imaging Archive (TCIA) in-vivo imaging data, caMicroscope pathology data, a pilot data set of animal model data, and relevant imaging annotation and markup data.

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The Importance of Data Standards

NCI CBIIT has worked extensively for several years in the area of data standards for both clinical research and healthcare, working with the community and Standards Development Organizations (SDOs), such as the Clinical Data Interchange Standards Consortium (CDISC), Health Level 7 (HL7) and the International Organization for Standardization (ISO). From that work, Enterprise Vocabulary Services (EVS) and Cancer Data Standards Registry and Repository (caDSR) are harmonized with the Biomedical Research Integrated Domain Group (BRIDG), Study Data Tabulation Model (SDTM), and Health Level Seven^® Reference Information Model HL7 RIM models. Standardized Case Report Forms (CRFs), including those for imaging, have also been created. The CBIIT project work provides the bioinformatics foundation for semantic interoperability in digital pathology and co-clinical trials integrated with clinical and patient demographic data and data contained in TCIA and TCGA.

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Digital Pathology and Integrated Query System

One of the goals of this sub-project is to create a digital pathology image server that can accept whole slide images from multiple vendors and display them despite the proprietary formats they were created in. They is accomplished by integrating the OpenSlide

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To address these limitations, the CTIIP team is developing an Integrated Query System to make it easier to analyze data from different research disciplines represented by TCGA, TCIA, and co-clinical/small animal model data. 

Digital Pathology

Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. In addition, each pathology-imaging vendor produces its own image management system, making image analysis systems proprietary and not standardized. The result is that images produced on different systems cannot be viewed and analyzed via the same mechanisms. Not only does this lack of standards and the dominance of proprietary formats impact digital pathology, but it prevents digital pathology data from integrating with data from other disciplines.

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With caMicroscope's support for basic image analysis algorithms, researchers can use this tool to enable analytic and decision support using digital pathology images.

Integrated Query System

The purpose of the integrative query component of CTIIP is to support data mashups between images, image-derived information, and clinical, pre-clinical, and genomic data. Co-clinical data and clinical data such as patient information and outcome will also be accessible through the Integrated Query System.

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The Integrated Query System, with its support for whole slides and data mashups of federated data, will act as a foundation for a broader set of novel community research projects.

DICOM Working Group 30

Since its first publication in 1993, DICOM has revolutionized the practice of radiology, allowing the replacement of X-ray film with a fully digital workflow. Each year, the standard is updated with formats for medical images that can be exchanged with the data and quality necessary for clinical use. (Source: http://dicom.nema.org/Dicom/about-DICOM.html)

As part of the Small Animal/Co-clinical Improved DICOM Compliance and Data Integration sub-project of CTIIP, the NCI supported the development of a DICOM supplement for small animal imaging. The group of people contributing to it, Working Group 30, completed Supplement 187: Preclinical Small Animal Imaging Acquisition Context

Supplement 187 Data Elements

Information about how a small animal image was acquired is relevant to the interpretation of the image and must be stored with it. While DICOM defines terminology applicable to other types of images, it does not include data elements associated with small animal image acquisition. The new Supplement 187, developed as part of the CTIIP project in 2015, defines terminology that is unique to small animal imaging. It includes the following templates that include terminology relevant to image acquisition.

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Consult Supplement 187: Preclinical Small Animal Imaging Acquisition Context

Pilot Challenges

Challenges are being increasingly viewed as a mechanism to foster advances in a number of fields, including healthcare and medicine. Large quantities of publicly available data, such as that in TCIA, and cultural changes in the openness of science have now made it possible to use these challenges, as well as crowdsourcing (enlisting the services of people via the Internet), to propel the field forward.

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A team from Massachusetts General Hospital will guide the Pilot Challenges. They will use are using Medical Imaging Challenge Infrastructure (MedICI), a medical imaging challenge platform, to support the challenges.   MedICI, in turn, uses the CodaLab framework, an open-source challenge platform developed by Microsoft Research and others in the medical imaging and machine learning communities. Because CodaLab does not have built-in imaging handling, display or annotation capabilities, the team will build is building on two application packages, ePad and caMicroscope, to provide those features. For example, once participants upload their results, they can see them in ePad.

Challenge participants receive test and training data by creating shared lists in TCIA, then pulling those into CodaLab. Once participants upload their results, they can see them in ePad.