Joint imaging platform for federated clinical data analytics

Scherer J, Nolden M, Kleesiek J, Metzger J, Kades K, Schneider V, Bach M, Sedlaczek O, Bucher AM, Vogl TJ, Gruenwald F, Kuehn JP, Hoffmann RT, Kotzerke J, Bethge O, Schimmoeller L, Antoch G, Mueller HW, Daul A, Nikolaou K, La Fougere C, Kunz WG, Ingrisch M, Schachtner B, Ricke J, Bartenstein P, Nensa F, Radbruch A, Umutlu L, Forsting M, Seifert R, Herrmann K, Mayer P, Kauczor HU, Penzkofer T, Hamm B, Brenner W, Kloeckner R, Duber C, Schreckenberger M, Braren R, Kaissis G, Makowski M, Eiber M, Gafita A, Trager R, Weber WA, Neubauer J, Reisert M, Bock M, Bamberg F, Hennig J, Meyer PT, Ruf J, Haberkorn U, Schoenberg SO, Kuder T, Neher P, Floca R, Schlemmer HP, Maier-Hein K (2020)

Publication Type: Journal article

Publication year: 2020

Journal

JCO Clinical Cancer Informatics American Society of Clinical Oncology

Book Volume: 4

Pages Range: 1027-1038

DOI: 10.1200/CCI.20.00045

Abstract

PURPOSE Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles. METHODS The Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions. RESULTS The JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform. CONCLUSION The results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data.

Involved external institutions

Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE) Ruprecht-Karls-Universität Heidelberg DE Germany (DE)

How to cite

APA:

Scherer, J., Nolden, M., Kleesiek, J., Metzger, J., Kades, K., Schneider, V.,... Maier-Hein, K. (2020). Joint imaging platform for federated clinical data analytics. JCO Clinical Cancer Informatics, 4, 1027-1038. https://doi.org/10.1200/CCI.20.00045

MLA:

Scherer, Jonas, et al. "Joint imaging platform for federated clinical data analytics." JCO Clinical Cancer Informatics 4 (2020): 1027-1038.

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