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The following table presents the data that the CTIIP team is integrating through various means. This integration relies on the expansion of software features and on the application of data standards, as described in subsequent sections of this document.
| Domain | Data Set |
|---|---|
| Clinical Imaging | The Cancer Genome Atlas (TCGA) clinical and molecular data |
| The Cancer Imaging Archive (TCIA) in-vivo imaging data | |
| Pre-clinical | Small animal models |
| Digital Pathology | caMicroscope |
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Furthermore, because each pathology-imaging vendor produces its own image management systems, these systems are also, by extension, proprietary and not standardized. The result is that images produced on different systems cannot be analyzed via the same mechanisms. In addition, no standard currently exists for (CKK: purpose of microAIM).
•DICOM for small animal research
–Long-term: generate DICOM compliant images vs. non-DICOM compliant images
•µAIM
–Developing the model
–Harmonization with AIM
àStandardized annotations and markup for radiology and pathology images
àImaging and BRIDG (beyond the scope of this project at this point)
•Improvements to the EVS vocabularies
Within these three research domains, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. 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, Within these three research domains, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. 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.
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, EVS and caDSR are harmonized with the BRIDG, SDTM, and 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 / TCGA.
Digital Pathology and Integrated Query System
Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. The result of this lack of uniformly accepted standards is that outside a given laboratory of small collaborative groups, the integration of pathology data with radiographic, genomic, and proteomic data is all but impossible.
This sub-project addresses the lack of uniformly accepted standards within digital pathology and the simultaneous need for integration of pathology data with radiographic, genomic, and proteomic data. Its mission is to create an open-source digital pathology image server that can host and serve digital pathology images for any of the major vendors without recoding, facilitating data integration. This image server would establish an informatics and IT infrastructure to implement pilot challenges for clinical and pre-clinical studies that integrate the (CKK: talk to Ulli about different names for the same? domains mentioned on this page) genomics, diagnostic imaging, and digital pathology domains.
Project 1: Integrated Query System for Existing TCGA Data
1) AIM 1 - Integrated query system for existing TCGA data (including improved pathology systems)
a) Histopathology
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•DICOM for small animal research
–Long-term: generate DICOM compliant images vs. non-DICOM compliant images
•µAIM
–Developing the model
–Harmonization with AIM
àStandardized annotations and markup for radiology and pathology images
àImaging and BRIDG (beyond the scope of this project at this point)
•Improvements to the EVS vocabularies
Digital Pathology and Integrated Query System
Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. The result of this lack of uniformly accepted standards is that outside a given laboratory of small collaborative groups, the integration of pathology data with radiographic, genomic, and proteomic data is all but impossible.
This sub-project addresses the lack of uniformly accepted standards within digital pathology and the simultaneous need for integration of pathology data with radiographic, genomic, and proteomic data. Its mission is to create an open-source digital pathology image server that can host and serve digital pathology images for any of the major vendors without recoding, facilitating data integration. This image server would establish an informatics and IT infrastructure to implement pilot challenges for clinical and pre-clinical studies that integrate the (CKK: talk to Ulli about different names for the same? domains mentioned on this page) genomics, diagnostic imaging, and digital pathology domains.
Goals: data exploration, data connection, data mashup, make data available for analysis, make data accessible for image algorithms
Project 1: Integrated Query System for Existing TCGA Data
1) AIM 1 - Integrated query system for existing TCGA data (including improved pathology systems)
a) Histopathology
i) Incorporate Openslide with caMicrosocope enabling caMicrosocope to directly serve whole slide pathology images from the majority of digital pathology vendors.
ii) Incorporate support for basic image analysis algorithms into caMicroscope.
iii) Standards-based image annotation utilizing the Annotation Image Markup (AIM) standard.
b) Integrative Queries
i) Programmatic Access to Data to TCGA-related image data.
ii) Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Histopathology
•Incorporate Openslide with caMicrosocope enabling caMicrosocope to directly serve whole slide pathology images from the majority of digital pathology vendors.
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•Incorporate support for basic image analysis algorithms into caMicroscope.
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•Standards
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-based image annotation utilizing the Annotation Image Markup (AIM) standard.
b) Integrative Integrative Queriesi) Programmatic
•Programmatic Access to Data to TCGA-related image data.
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•Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Integrative Query System
Look at Ulli's PPT
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Co-clinical and animal model images: most imaging machines for animal model imaging do not follow the DICOM standard. We developed a supplement to the DICOM standard to accommodate small animal imaging (standard out for balloting). We want to include co-clinical/animal model data in the integrative queries. For this new standard to be used, equipment manufacturers would need to incorporate this standard when they develop machines/software/software.
AIM 2: TCGA Infrastructure Ported to/applied to Co-clinical Setting
•Improve small-animal DICOM compliance
•Identify co-clinical pilot data set and populate integrated ‘omics/imaging infrastructure.
Challenges
Solutions
DICOM Working Group 30
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iv) Enable community sharing of algorithms on a software clearinghouse platform such as HubZero.
Three pilot challenges–pathology, radiology, co-clinical.
Medical Image Computational and computer-assisted Intervention: MICCAI
Interventions in tumors, cardiology, etc that are image-based
Mass General will guide the pilots
Ground truth: find the compatibility of the informatics that we need to run pilots. Take images out of TCIA, CGA, clinical data and compare them.
Jasharee doing MICCAI Challenge in Munich. Segmentation of nuclear imaging in pathology. Combined radiology and pathology classification.
Want to be able to say that these informatics allow us to compare the pathology, rad, co-clinical findings.
Document the approach, technology, application to do a MICCAI challenge the way Jaysharee does it. See their order of march.
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HubZero.
Three pilot challenges–pathology, radiology, co-clinical.
Medical Image Computational and computer-assisted Intervention: MICCAI
Interventions in tumors, cardiology, etc that are image-based
Mass General will guide the pilots
Ground truth: find the compatibility of the informatics that we need to run pilots. Take images out of TCIA, CGA, clinical data and compare them.
Jasharee doing MICCAI Challenge in Munich. Segmentation of nuclear imaging in pathology. Combined radiology and pathology classification.
Want to be able to say that these informatics allow us to compare the pathology, rad, co-clinical findings.
Document the approach, technology, application to do a MICCAI challenge the way Jaysharee does it. See their order of march.
Challenges: read one-page document. We want to use pathology images in the challenges. The tool used to display the markup and annotations (for the pathology images) is caMicroscope. There will be a challenge in which animal model data will be used. Give people images they have never seen before and develop algorithms (like to circle all the nuclei). Ground truth decided by a pathologist and a radiologist. The algorithm that comes closest to ground truth is the winner.
Compare the decision support systems for three imaging research domains: Clinical Imaging, Pre-clinical Imaging, and Digital Pathology
•Leverage and extend the above platform and data systems to validate and share algorithms, support precision medicine and clinical decision-making tools, including correlation of imaging phenotypes with genomics signatures. The aims are fashioned as four complementary “Pilot Challenges”.
Four complimentary pilot challenges:
Clinical Imaging:
•QIN image data for several modalities/organ systems are already hosted on TCIA. 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:
•Leveraging 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 Clinical Support:
•Leveraging Aims1-3 develop open source image analysis algorithms which complement ‘omics data sets and provide additional decision support.
Community Sharing:
•Enable community sharing of algorithms on a software clearinghouse platform such as HubZero.
Challenge Management System, MedICI
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