 |
Page History
...
The sub-projects, along with the solutions they provide, are discussed in this guide and listed below.
| Sub-Project Name | GoalSolution it Provides |
|---|---|
| Digital Pathology and Integrated Query System | Address the interoperability of digital pathology data, improve integration and analytic capabilities between TCIA and TCGA, and raise 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 | Address the need for standards in pre-clinical imaging and test the informatics created in the Digital Pathology and Integrated Query System sub-project for decision support in co-clinical trials. |
| Pilot Challenges | 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 intent is not to specifically implement a rigorous “Grand Challenge,” but rather to develop pilot challenge projects. These would use limited data sets for proof-of-concept, and test the informatics infrastructure needed for such “Grand Challenges” that would later be scaled up and supported by extramural initiatives later in 2014 and beyond. |
Data Standards Applicable to CTIIP
The result will be that allow researchers to create queries combining attributes from molecular, imaging, and clinical data, and to use such integrated queries to explore, filter, and select data for their driving biological problems.
Informatics helps us communicate. It can help us better treat our patients.
This common infrastructure depends on data interoperability, which is greatly aided by adherence to data standards. While standards such as Annotation and Image Markup (AIM) and Digital Imaging and Communications in Medicine (DICOM) exist, vendors of data viewers and other tools required for the analysis of imaging data have not widely adopted them. Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. While DICOM has published a digital pathology standard, the major vendors in this space have not widely adopted the standard.
This common infrastructure depends on data interoperability, which requires adherence to data standards. Data standards ensure that While standards such as DICOM and AIM exist, vendors of data viewers and other tools required for data analysis have not widely adopted them.
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.
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. The lack of standards in pre-clinical and pathology prevents the ability to share and leverage data across studies and institutions.
Furthermore, because each pathology-imaging vendor produces its 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 markup and annotations on images.
Each domain is at a different step in maturity. proprietary data formats
- The lack of standards in pre-clinical and pathology prevents the ability to share and leverage data across studies and institutions.
- There are differences between the domains, and therefore there should be careful consideration of where there are commonalities in semantic interoperability, and where there is not.
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 is harmonized with the BRIDG, SDTM, and HL7 RIM models. Standardized Case Report Forms (CRFs), including those for imaging, have also been created. This 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.
How do we better treat our patients?
This is highly consistent with the research goals of the Informatics Imaging Working Group, the needs raised at the Imaging Informatics Workshop in March 2013, and the mission of CBIIT, and leverages critical resources and previous NCI investments to target important cancer problems, such as clinical decision support for predicting or assessment of response to therapy. All of these goals are consistent with the NCI BSA recommendations for CBIIT and the NCI focus on precision medicine.
Three separate sections with problem/solution for each aim. Status of the solution.
Informatics have to let us communicate. Need to be able to compare the data between the omics.
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 is 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 / TCGAThe overarching goal of this project is to establish an informatics infrastructure that demonstrates the benefit and feasibility of data interoperability across the three domains: Genomics, Diagnostic Imaging, and Digital Pathology. The intent is to identify and address the interoperability needs to support specific research objectives, with the goal of demonstrating the need to scale up. The scope is limited to pilot data sets, and the intent is only to demonstrate the infrastructure. Creation of more robust tools that leverage the interoperability and infrastructure created in this project would be supported through extramural support after the benefit of scaling up has been demonstrated.
Digital Pathology and Integrated Query System
...
