The Co-Clinical Imaging Research Resources Program Network (CIRP) is based on the trans-NCI initiative, currently, PAR-18-841. This FOA invites Cooperative Agreement applications to develop research resources that encourage a consensus on how quantitative imaging methods are optimized to improve the quality of imaging results for co-clinical trials. Projects include optimization of pre-clinical quantitative imaging methods, implementation in co-clinical trials, and creating a web-accessible research resource that contains all the data, methods, workflow documentation, and results collected from cancer therapeutic or prevention co-clinical investigations. To achieve the goals of the CIRP, applicants are encouraged to organize multi-disciplinary teams with experience in mouse models research, human investigations, imaging platforms, quantitative imaging methods, decision support software and informatics to populate the research resource. Each resource contains four essential elements: animal models, co-clinical trials, quantitative imaging, and informatics.
The Co-clinical Imaging Research Resource Program Network (CIRP) is organized to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging and developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy.
Washington University at St Louis
PI: Kooresh Shoghi (email@example.com )
PI: Cristian Badea (Cristian.Badea@duke.edu)
PI: Henry Charles Manning (firstname.lastname@example.org)
Project: VU Predict
University of Pennsylvania
PI: Rong Zhou (email@example.com)
Washington University at St Louis: https://c2ir2.wustl.edu/
Duke University: https://sites.duke.edu/pcqiba/
Vanderbilt University: https://vu-predict.app.vumc.org/
University of Pennsylvania: https://www.med.upenn.edu/PennU24Resource/
2019 WMIC Meeting, Sept 4 - 7, 2019, Montreal, Canada, Spotlight Session 5: Co-clinical Imaging in Precision Medicine