Abstract Artificial intelligence (AI) and other computationally intensive methods have the potential to revolutionize cancer imaging research and patient care. The broad adoption of these technologies depends on the availability development of imaging informatics tools to assist users in managing massive data sets, generating well-curated annotations, and accessing scalable computing resources. The Integrative Imaging Informatics for Cancer Research (I3CR) Center has played a lead role in implementing cancer imaging informatics technology, with a focus on expanding the widely used open source XNAT informatics platform to better support computational workflows in cancer imaging. I3CR has also developed knowledge management tools to better track data processing and analysis, including tools for orchestrating and tracking container-based computing pipelines. As result of this work, XNAT has emerged as the most widely used imaging informatics platform in cancer research. It is deployed in over 200 organizations across academia and industry and has been adopted across a wide range of research contexts, including preclinical imaging, multi-site clinical trials, and clinical translation. As the I3CR informatics platform has matured and been widely adopted, the Center is now evolving to the next phase of development to more broadly sustain the platform. In the work proposed here, we will sustain the I3CR’s ongoing engineering initiatives and expand its outreach efforts. The Center’s sustainment activities will build on and extend the I3CR platform’s expansive set of data and knowledge management capabilities, with a focus on addressing key emergent needs within our user base. In Aim 1, we will continue to develop the XNAT-based data management platform, including adding standards-based clinical interfaces, developing a cohort discovery service with natural language processing support, implementing a task automation service, and extending its container-based computing service to support high performance computing and cloud computing environments. In Aim 2, we will implement a suite of integrations with complementary image analysis and data sharing platforms, including The Cancer Image Archive and the NCI Imaging Data Commons. In Aim 3, we will expand the I3CR’s outreach initiatives to ensure broad and effective adoption of the I3CR informatics platforms. The Center’s training program will utilize the XNAT Academy education platform to host a series of online training programs directed at specific audiences including developers, data scientists, integrators, and system administrators. A suite of supporting cloud services will be implemented assist the community in adopting and developing on the I3CR platform.

Relevance Medical imaging is one of the key methods used by cancer researchers to study human biology in health and disease. The imaging informatics platform described in this application will enable cancer researchers to capture, analyze, and share imaging and related data. These capabilities address key bottlenecks in the pathway to discovering cancer cures and treatments.