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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. 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 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, 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).
CTIIP Sub-Projects
As discussed so far, cancer research is needed across domains. 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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| Sub-Project Name | Description |
|---|---|
| Digital Pathology and Integrated Query SystemCTIIP Primer (DRAFT) | Addresses the interoperability of digital pathology data, improves integration and analytic capabilities between TCIA and TCGA, and raises the level of interoperability to create the foundation required for pilot demonstration projects in each of the targeted research domains: clinical imaging, pre-clinical imaging, and digital pathology imaging. |
| DICOM Standards for Small Animal Imaging; Use of Informatics for Co-clinical Trials | Addresses the need for standards in pre-clinical imaging and tests the informatics created in the Digital Pathology and Integrated Query System sub-project for decision support in co-clinical trials. |
| Pilot ChallengesCTIIP Primer (DRAFT) | Challenges will be designed to develop knowledge-extraction tools and compare decision-support systems for the three research domains, which will now be represented as a set of integrated data from TCIA and TCGA. The pilot challenges would use limited data sets for proof-of-concept, and test the informatics infrastructure needed for more rigorous “Grand Challenges” that could later be scaled up and supported by extramural initiatives. |
The Importance of Data Standards
The common infrastructure that will result from CTIIP and its sub-projects depends on data interoperability, which is greatly aided by adherence to data standards. While image data standards exist to support communicating image data in a common way, the data standards that do exist for image data are inconsistently adopted. One reason for the lack of uniform adoption is that vendors of image management tools required for the analysis of imaging data have created these tools so that they only accept proprietary data formats. Researchers then make sure their data can be interpreted by these tools. The result is that images produced on different systems cannot be analyzed via the same mechanisms.
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Within the three research domains that CTIIP intends to make available for integrative queries, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. Those standards include Annotation and Image Markup (AIM), which allow researchers to standardize annotations and markup for radiology and pathology images, and Digital Imaging and Communications in Medicine (DICOM), which is a standard for handling, storing, printing, and transmitting information in medical imaging. For pre-clinical imaging and digital pathology, there are no such standards that allow for the seamless viewing, integration, and analysis of disparate data sets to produce integrated views of the data, quantitative analysis, data integration, and research or clinical decision support systems.
As part of the DICOM Standards for Small Animal Imaging; Use of Informatics for Co-clinical Trials sub-project, the long-term goal is to generate DICOM-compliant images for small animal research. MicroAIM (µAIM) is currently in development to serve the unique needs of this domain.
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| Domain | Data Set | Applicable Standard |
|---|---|---|
| Clinical Imaging | The Cancer Genome Atlas (TCGA) clinical and molecular data | DICOM |
| Clinical Imaging | The Cancer Imaging Archive (TCIA) in vivo imaging data | DICOM |
| Pre-Clinical | Small animal models | N/A MicroAIM in development |
| Digital Pathology | caMicroscope | DICOM |
| All | Annotations and markup on images | AIM |
Digital Pathology and Integrated Query System
The goal of this foundational sub-project is to create a digital pathology image server that can accept images from multiple domains and run integrative queries on that data. Using this server, which is an extended version of caMicroscope, researchers can select data from different imaging data sets and use them in image algorithms. The first data sets that are being integrated on this image server are TCGA and TCIA.
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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. The lack of common data standards will not be a hindrance to data analysis, since the server that the unified query interface is on will accept whole slides without recoding. The unified query interface will also provide a common platform and data engine for the hosting of “pilot challenges," which are described in more detail below. Pilot challenges will advance biological and clinical research in a way that also integrates the clinical, co-clinical/small animal model, and digital pathology imaging disciplines.
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 systems, making image analysis systems proprietary and not standardized. The result is that images produced on different systems cannot be 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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Image annotations also require standards so that they can be read by different imaging disciplines along with the rest of the image data. caMicroscope will also be extended to include standards-based image annotation using the Annotation Image Markup (AIM) standard.
Integrated Query System
To make data comparable, it must first be collected in a structured fashion. For example, TCGA relies on Common Data Elements, which are the standard elements used to validate TCGA clinical data. Second, data comparisons require common data standards. For example, when a tumor is described in a human or an animal, a data standard would require that the type of tumor match one of a discrete number of options using approved vocabulary, such as "brain".
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Given the technical challenges inherent in such a system, technical solutions are being developed. One of the most fundamental to the success of the Integrated Query System is an Application Program Interface (API) that provides a Representational State Transfer (REST) API to TCIA metadata and image collections. This API is built using a middleware platform called Bindaas that also forms the backend infrastructure of caMicroscope. Bindaas is open source and extensible, so can be expanded to include more data types and additional integration. This API is being designed to support federation of multiple information repositories using the concept of data mashups. A data mashup in this case is a software interface, much like a dashboard, that allows a person to visualize and analyze data from different sources. The Integrated Query System, with its support for whole slides and data mashups, will act as a foundation for a broader set of novel community research projects.
Small Animal/Co-clinical Improved DICOM Compliance and Data Integration
While the challenges of integrating small animal/co-clinical data with data on humans are steep, given the lack of common data standards, the potential rewards are great. These rewards depend on a common data standard for human and small animal data and support by equipment manufacturers for the standard.
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With small animal/co-clinical data meeting the DICOM standard, researchers could find a mouse with the same kind of tumor and compare its response to various therapies that could help generate sophisticated diagnoses and treatment plans.
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 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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| Discipline | Pilot Challenge |
|---|---|
| Clinical Imaging | Pilot challenge projects are being explored for X-ray CT, DWI MRI and PET CT similar to the HUBzero pilot CT challenge project. |
| Pre-clinical / Co-clinical Imaging | Leverage the Mouse Models of Human Cancer Consortium (MHHCC) Glioblastoma co-clinical trials with associated omics data sets from the Human Brain Consortium. This proof-of-concept will focus on bringing together omics and imaging data into a single platform. |
| Digital Pathology | Develop open source image analysis algorithms that complement omics data sets and provide additional decision support. |
Comparing Algorithms to Ground Truth
Before a challenge begins, the Pilot Challenge team will work with a pathologist and a radiologist to determine the ground truth for a particular image. Participants will then analyze images they have never seen before and develop algorithms to accomplish a certain task. The algorithm that comes closest to ground truth is the winner of the challenge.
MedICI
Jaysharee's program: Medical Imaging Challenge Infrastructure: MedICI
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Once participants upload their results, they can see them in ePad.
Notes
Medical Image Computational and computer-assisted Intervention: MICCAI
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Document the approach, technology, application to do a MICCAI challenge the way Jaysharee does it.
Scenarios
Need to generate proper therapy for a patient. Look at in vivo imaging, radiology and pathology, run a gene panel to look for abnormal. Look at co-clinical trials (model of a tumor in a mouse that is similar to a human. Experiment therapies on mice.) Run an integrative query to develop a sophisticated diagnosis. Search big data.
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