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Imaging-based cancer research is ushering in the beginning phase of an integrative-biology revolution. It is now feasible to extract large sets of quantitative image features relevant to cancer prognosis or treatment across three complementary research domains: clinical imaging, pre-clinical imaging, and digital pathology. These high-dimensional image feature sets can be used to infer clinical phenotypes or correlate with gene–protein signatures. This type of analysis, however, requires large volumes of data.
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CTIIP is composed of the following sub-projects. Each project is discussed in on this documentpage.
| Sub-Project Name | Description |
|---|---|
| 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 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. |
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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 standards such as Annotation and Image Markup (AIM) and Digital Imaging and Communications in Medicine (DICOM) While data standards exist to support images, vendors of data viewers and other tools required for the analysis of imaging data have not widely adopted them. 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 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
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 |
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.
communicating image data in a common way, these data standards are adhered to in a variable way. 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. The result is that images produced on different systems cannot be analyzed via the same mechanisms.
Another challenge for CTIIP with its goal of being to integrate data from complimentary domains is the lack of a defined standard for pre-clinical and digital pathology data. This makes it very difficult to share and leverage these kind of data across studies and institutions.
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, 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.
Where data standards do exist, the NCI and the CTIIP team aim to promote them. The following work is
such as Annotation and Image Markup (AIM) and Digital Imaging and Communications in Medicine (DICOM)
•DICOM for small animal research
–Long-term: generate DICOM compliant images vs. non-DICOM compliant images
•µAIM
–Developing the model
–Harmonization with AIM
Standardized annotations and markup for radiology and pathology images
Imaging and BRIDG (beyond the scope of this project at this point)
•Improvements to the EVS vocabularies
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 |
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.
•DICOM for small animal research
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