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TOMOGRAPHY, December 2016, Volume 2, Issue 4: 242-249
DOI: 10.18383/j.tom.2016.00265

Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network

Keyvan Farahani1, Jayashree Kalpathy-Cramer2, Thomas L. Chenevert3, Daniel L. Rubin4, John J. Sunderland5, Robert J. Nordstrom1, John Buatti6, and Nola Hylton7

1Cancer Imaging Program, National Cancer Institute, Bethesda, Maryland; 2Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; 3Department of Radiology, University of Michigan, Ann Arbor, Michigan; 4Department of Radiology, Biomedical Data Science, and Medicine (Biomedical Informatics Research), Stanford University, Palo Alto, California; 5Department of Radiology, University of Iowa, Iowa City, Iowa; 6Department of Radiation Oncology, University of Iowa, Iowa City, Iowa; and 7Department of Radiology, University of California San Francisco, San Francisco, California

Abstract

The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image assessment parameters through network-wide cooperative projects. To more effectively use the cooperative power of the network in conducting computational challenges in benchmarking of tools and methods and collaborative projects in analytical assessment of imaging technologies, the QIN Challenge Task Force has developed policies and procedures to enhance the value of these activities by developing guidelines and leveraging NCI resources to help their administration and manage dissemination of results. Challenges and Collaborative Projects (CCPs) are further divided into technical and clinical CCPs. As the first NCI network to engage in CCPs, we anticipate a variety of CCPs to be conducted by QIN teams in the coming years. These will be aimed to benchmark advanced software tools for clinical decision support, explore new imaging biomarkers for therapeutic assessment, and establish consensus on a range of methods and protocols in support of the use of quantitative imaging to predict and assess response to cancer therapy.

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