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Imaging-based cancer research is in the beginning phase of an Integrative-biology revolution. It is now feasible to extract large sets of quantitative image features relevant to prognosis or treatment across three complementary research domains: in vivo clinical imaging, pre-clinical imaging, and digital pathology. These high-dimensional image feature sets can be used to infer clinical phenotypes or correlate with gene–protein signatures. This type of analysis, however, requires large volumes of data. In this project, we propose to develop and deploy software that supports a comprehensive and reusable exploration and fusion of imaging, clinical, and molecular data. TCGA/TCIA provides a rich multi-domain dataset in an infrastructure that provides limited query capability for identifying cases based on all the data types available. Cross-disciplinary analysis would be facilitated by providing a unified query interface. This infrastructure would then be applied to clinical -co-clinical settings and provide a common platform and data engine for hosting of “pilot challenges”. An opensource software clearinghouse will enable community sharing of algorithms used in the analyses.
Project 1
1) AIM 1 - Integrated query system for existing TCGA data (including improved pathology systems)
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Problem Statement
Approach
Project 3
Problem Statement
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1) AIM 3 - “Pilot Challenges” to compare the decision support systems for three imaging research domains: Clinical Imaging, Pre-clinical Imaging, and Digital Pathology.
a) Leverage and extend the above platform and data systems to validate and share algorithms, support precision medicine and clinical decision making tools, including correlation of imaging phenotypes with genomics signatures. The aims are fashioned as four complementary “Pilot Challenges”.
i) Clinical Imaging: QIN image data for several modalities/organ systems are already hosted on TCIA. Pilot challenge projects are being explored for X-ray CT, DWI MRI and PET CT similar to the HUBzero pilot CT challenge project.
ii) Pre-clinical / Co-clinical Imaging leveraging the Mouse Models of Human Cancer Consortium (MHHCC) Glioblastoma co-clinical trials with associated ’omics data sets from the Human Brain Consortium. This proof of concept will focus on bringing together ‘omics and imaging data into a single platform.
iii) Digital Pathology clinical support. Leveraging Aims1-3 develop open source image analysis algorithms which complement ‘omics data sets and provide additional decision support.
iv) Enable community sharing of algorithms on a software clearinghouse platform such as HubZero.
Problem Statement
Approach
Conclusion
The result will be a set of open source software tools that allow researchers to create queries combining attributes from molecular, imaging, and clinical data, and to use such integrated queries to explore, filter, and select data for their driving biological problems. The impact on integrative research projects such as co-clinical trials would be to give researchers the ability to directly compare data from pre-clinical animal models with real-time clinical data. The pilot challenge projects will proactively facilitate biological and clinical research across three NCI divisions. This is highly consistent with the research goals of the Informatics Imaging Working Group, the needs raised at the Imaging Informatics Workshop in March 2013, and the mission of CBIIT, and leverages critical resources and previous NCI investments to target important cancer problems, such as clinical decision support for predicting or assessment of response to therapy. All of these goals are consistent with the NCI BSA recommendations for CBIIT and the NCI focus on precision medicine. The approach taken to development in this project emphasizes modular semantic interoperability and open source tooling, making it immediately valuable to scientists with NCI funded research networks in the three research domains, as well as the national and international research communities, and providing a framework for enhanced adoption of these methods by biologists in the larger genomics/proteomic communities.
Put together a primer, examples of data, use cases, how to carry out an integrative query, so that it is understandable.
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Business Benefits
Summary
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Conclusion
The National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) seeks to increase the understanding of genetic mutations to better diagnose and treat patients with cancer. Its component projects support research goals from the domains of genomics, diagnostic imaging, and digital pathology. These research goals include:
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