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Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. In addition, each pathology-imaging vendor produces its own image management systems, making image analysis systems proprietary and not standardized. The result is that images produced on different systems cannot be analyzed via the same mechanisms. Not only does this lack of standards and the dominance of proprietary formats impact digital pathology, but it prevents digital pathology data from integrating with radiographic, genomic, and proteomic datadata from other disciplines.

The purpose of the digital pathology component of CTIIP is to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA. The team is using OpenSlide, a vendor-neutral C library, to extend the software of caMicroscope, a digital pathology server, to provide the infrastructure for these data mashups. The extended software will support some of the common formats adopted by whole slide vendors as well as basic image analysis algorithms. With the incorporation of common whole slide formats, caMicroscope will be able to read whole slides without recoding, which often introduces additional compression artifacts. With the addition of support for basic image analysis algorithms, (CKK: the following things can happen...). These additional features of caMicroscope will make it possible to integrate digital pathology images within TCIA and NBIA and provide a logical bridge from proprietary pathology formats to DICOM standards.

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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 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 patienthelp 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 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.

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)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?

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