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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.

The goal of the Small Animal/Co-clinical Improved DICOM Compliance and Data Integration sub-project is to directly compare data from co-clinical animal models to real-time clinical data from TCGA. The team will accomplish this by applying the TCGA infrastructure common data elements used in TCGA, such as , to a co-clinical data set. Specifically, this sub-project will:

• Develop a supplement to the DICOM standard to accommodate small animal imaging
• Identify a pilot co-clinical data set to integrate with TCIA and TCGAa pilot co-clinical data set to integrate with TCIA and TCGA

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. This goal depends on a common data standard and support by equipment manufacturers for the standard. For example, consider the following research question, made possible through increased DICOM compliance by small animal/co-clinical data.

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Improving the compliance of co-clinical data with DICOM will help improve doctors' ability to make firm diagnoses.We are pulling data out of caDSR (ER negative and positive, other common data elements) and we are asking Bob Cardiff's team to ask the same questions so that we can compare human and mouse data.

Pilot Challenges

1)      AIM 3 - “Pilot Challenges” to compare the decision support systems for three imaging research domains: Clinical Imaging, Pre-clinical Imaging, and Digital Pathology.

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