ImagingSD1

There is strong consensus that phenotypic imaging data will add meaningful discovery in the research setting and to diagnosis, staging, and prognosis in the clinical setting when cross correlated with molecular profiling and pathology data.  

scientific medical researcher

1.    Grid data services to enable the sharing of DICOM RT Objects, clinical data, outcome data, associated metadata and any annotations and markup created by reviewers.
2.    These grid services would also require mechanisms to support role-based data access controls that utilize grid credentials
3.    A deployment of the caGrid security infrastructure (GAARDS) and integration with the institutional identity providers of the participating institutions.
4.    Information models to better describe the data that is being shared on the grid. This includes AIM extensions, extensions to the NCIA DICOM data model to support DICOM Objects and models to describe outcome data.
5.    Grid analytical services to support on-demand deployment and invocation of review and analysis.
6.    Grid middleware that federates the various data services allowing exploratory queries that span multiple data models.
7.    Workflow execution framework that allows researchers to obtain data from the federated data services, discover analysis algorithms relevant to the data archive, construct and execute a workflow of the various analysis routines
8.    Integration with CTMS and clinical data systems,
9.    Creation of a spanning ontology that supports a single common reference information model for all caBIG projects that need to refer to imaging and other DICOM-related information in their individual information
10.    Extensions of AIM to new domains such as RT and microscopy
11.    All relevant patient data needs to be mapped to a common Patient ID structure.
12.    Well annotated Imaging and Microscopy files are available at NBIA.

The clinical researcher is able to view the radiology, histopathology, proteomics and clinical data on the queried upon criteria

1.    A translation scientist logs into the data query service
2.    He specifies key words that he is looking for on the grid
3.    The service takes up the keywords and queries NCIA, caHUB, caArray for imaging, histopathology, clinical and genomics data
4.    The query performs a join of the result based on common patient ID structure and displays it to the scientist.

None

High

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