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Small Animal/Co-clinical Improved DICOM Compliance and Data Integration

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 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 TCGA

While the challenges of integrating small animal/co-clinical data with data on humans are steep, given the 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.

• If you treat a mouse with an estrogen-receptor (ER) negative tumor with a certain drug, how does the outcome compare to that of a human with the same tumor and ER status?

Researchers could also scenario of wanting to generate effective therapy for a cancer patient. With an integrated query system, researchers could search in-vivo imaging, radiology, and digital pathology data from the patient, run a gene panel, and identify abnormal genes in the patient. With small animal/co-clinical data meeting the DICOM standard, researchers could find a mouse with the same kind of tumor and compare its response to various therapies that could help generate sophisticated diagnoses and treatment plans.

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 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 TCGA

Improving the compliance of co-clinical data with DICOM will help improve doctors' ability to make firm diagnoses.

Use case: Breast cancer has biomarkers (progesterone status, etc.). One question to ask is "if the estrogen status is negative in humans, what does the pathology look like?" Then compare this to mice. Is the model we have a good model for the human condition?

If you treat a mouse model that has an ER negative status with a certain drug, what is the outcome? Then see this in humansImproving 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.

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