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National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging
Andrey Fedorov1, William J R Longabaugh1, David Pot1
1From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.).
Abstract:
The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts large and diverse publicly available cancer image data collections. By harmonizing all data based on industry standards and colocalizing it with analysis and exploration resources, the IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. Published under a CC BY 4.0 license.
