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Image annotations also require standards so that they can be read by different imaging disciplines along with the rest of the image data. caMicroscope will also be extended to include standards-based image annotation using the Annotation Image Markup (AIM) standard.

This infrastructure can be expanded to include more data types and additional integration, which will provide analytic and decision support to researchers, who can then pursue a broader set of novel community research projects.

Integrated Query System

To make data comparable, it must first be collected in a structured fashion. For example, TCGA relies on Common Data Elements, which are the standard elements used to validate TCGA clinical data. Second, data comparisons require common data standards. For example, when a tumor is described in a human or an animal, a data standard would require that the type of tumor match one of a discrete number of options using approved vocabulary, such as "brain".

The integrated query system currently in development will serve as an archive of images from multiple imaging disciplines, shown below.

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Given the technical challenges inherent in such a system, technical solutions are being developed. This requires technical solutions. One of the most fundamental to the success of the integrated query system is an Application Program Interface (API) that provides a Representational State Transfer (REST) API to TCIA metadata and image collections. This API is built using a middleware platform called Bindaas that is also used to build the backend infrastructure of caMicroscope. This API is being designed to support federation of multiple information repositories using the concepts of a data mashups. This infrastructure can be Because this infrastructure is open source and extensible, it can be expanded to include more data types and additional integration, and as well as provide analytic and decision support, which will act as a foundation for a broader set of novel community research projects.

The recently concluded FDA Imaging Pilot demonstrated the feasibility of data federation between NBIA and AIM, and the value of such data federation in streamlining the imaging review processes at the FDA. This data federation is made possible by the Bindaas middleware that is also used to build the backend infrastructure of caMicroscope. We propose to extend this backend infrastructure with a data federation capability that provides for query capability using multiple data points across TCIA and TCGA will be implemented.

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Relate data from TCIA, caMicroscope, animal model

how do we make a decision on a firm diagnosis?

Get queries and relate it to the human data and vice versa

System should integrate clinical data (from TCGA), preclinical data (comes from UC Davis)

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 humans.

We are setting up the data structure so when that is done, we'll be able to see what use cases are possible.

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.

If you did an integrative query, how would you do it? Data calls to do different integrative queries. How would you use sufficient standard data. Come out with information that will allow you to make a decision. Pilot challenges to compare the decision support systems for three domains.

Pathology problems:1.  proprietary data formats that cannot be displayed and manipulated in the same tools. Solution is to integrate caMicroscope with OpenSlide (allows us to read prop. formats without converting images). Makes a large number of image formats accessible. 2. no standard for markups and annotations. So we're creating microAIM.

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

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. This goal depends on a common data standard and support by 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. 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 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 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. 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.

integrated queries between animal and human data would be an option for generating sophisticated diagnoses and treatment plans.

how do we make a decision on a firm diagnosis?

Get queries and relate it to the human data and vice versa

System should integrate clinical data (from TCGA), preclinical data (comes from UC Davis)

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 humans.

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 dataWith 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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Challenge Management System, MedICI

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