Page History

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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 tool vendors equipment manufacturers for the standard. For example, consider the 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 eventually benefit the human patient.

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. The team will accomplish this by applying the TCGA infrastructure to a co-clinical data set.

Developing DICOM standards for small animal imaging and identify co-clinical datasets to test the integration of TCIA and TCGA for this data.

TCGA Infrastructure Applied to Co-Clinical

1)      AIM 2 - TCGA infrastructure ported to/applied to co-clinical setting 

a)       Pilot improve small-animal DICOM compliance

b)       Identify co-clinical pilot data set and populate integrated ‘omics/imaging infrastructure.

Co-clinical and animal model images: most imaging machines for animal model imaging do not follow the DICOM standard. We developed a supplement to the DICOM standard to accommodate small animal imaging (standard out for balloting). We want to include co-clinical/animal model data in the integrative queries. For this new standard to be used, equipment manufacturers would need to incorporate this standard when they develop machines/software.

AIM 2: TCGA Infrastructure Ported to/applied to Co-clinical Setting

Challenges

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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.

With small animal/co-clinical data meeting the DICOM standard, integrated queries between animal and human data would be an option for generating sophisticated diagnoses and treatment plans.

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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Once participants upload their results, they can see them in ePad.

Challenges

Solutions

Scenarios

Need to generate proper therapy for a patient. Look at in vivo imaging, radiology and pathology, run a gene panel to look for abnormal. Look at co-clinical trials (model of a tumor in a mouse that is similar to a human. Experiment therapies on mice.) Run an integrative query to develop a sophisticated diagnosis. Search big data.

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