Joint Imaging Platform for Federated Clinical Data Analytics

Jonas Scherer; Marco Nolden; Jens Kleesiek; Jasmin Metzger; Klaus Kades; Verena Schneider; Michael Bach; Oliver Sedlaczek; Andreas M Bucher; Thomas J Vogl; Frank Grünwald; Jens-Peter Kühn; Ralf-Thorsten Hoffmann; Jörg Kotzerke; Oliver Bethge; Lars Schimmöller; Gerald Antoch; Hans-Wilhelm Müller; Andreas Daul; Konstantin Nikolaou; Christian la Fougère; Wolfgang G Kunz; Michael Ingrisch; Balthasar Schachtner; Jens Ricke; Peter Bartenstein; Felix Nensa; Alexander Radbruch; Lale Umutlu; Michael Forsting; Robert Seifert; Ken Herrmann; Philipp Mayer; Hans-Ulrich Kauczor; Tobias Penzkofer; Bernd Hamm; Winfried Brenner; Roman Kloeckner; Christoph Düber; Mathias Schreckenberger; Rickmer Braren; Georgios Kaissis; Marcus Makowski; Matthias Eiber; Andrei Gafita; Rupert Trager; Wolfgang A Weber; Jakob Neubauer; Marco Reisert; Michael Bock; Fabian Bamberg; Jürgen Hennig; Philipp Tobias Meyer; Juri Ruf; Uwe Haberkorn; Stefan O Schoenberg; Tristan Kuder; Peter Neher; Ralf Floca; Heinz-Peter Schlemmer; Klaus Maier-Hein

doi:10.1200/CCI.20.00045

Joint Imaging Platform for Federated Clinical Data Analytics

Jonas Scherer, Marco Nolden, Jens Kleesiek, Jasmin Metzger, Klaus Kades, Verena Schneider, Michael Bach, Oliver Sedlaczek, Andreas M Bucher, Thomas J Vogl, Frank Grünwald, Jens-Peter Kühn, Ralf-Thorsten Hoffmann, Jörg Kotzerke, Oliver Bethge, Lars Schimmöller, Gerald Antoch, Hans-Wilhelm Müller, Andreas Daul, Konstantin NikolaouChristian la Fougère, Wolfgang G Kunz, Michael Ingrisch, Balthasar Schachtner, Jens Ricke, Peter Bartenstein, Felix Nensa, Alexander Radbruch, Lale Umutlu, Michael Forsting, Robert Seifert, Ken Herrmann, Philipp Mayer, Hans-Ulrich Kauczor, Tobias Penzkofer, Bernd Hamm, Winfried Brenner, Roman Kloeckner, Christoph Düber, Mathias Schreckenberger, Rickmer Braren, Georgios Kaissis, Marcus Makowski, Matthias Eiber, Andrei Gafita, Rupert Trager, Wolfgang A Weber, Jakob Neubauer, Marco Reisert, Michael Bock, Fabian Bamberg, Jürgen Hennig, Philipp Tobias Meyer, Juri Ruf, Uwe Haberkorn, Stefan O Schoenberg, Tristan Kuder, Peter Neher, Ralf Floca, Heinz-Peter Schlemmer, Klaus Maier-Hein

Clinic of Radiology and Nuclear Medicine

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.

Original language	English
Journal	JCO clinical cancer informatics
Volume	4
Pages (from-to)	1027-1038
Number of pages	12
ISSN	2473-4276
DOIs	https://doi.org/10.1200/CCI.20.00045
Publication status	Published - 11.2020

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10.1200/CCI.20.00045

Cite this

Scherer, J., Nolden, M., Kleesiek, J., Metzger, J., Kades, K., Schneider, V., Bach, M., Sedlaczek, O., Bucher, A. M., Vogl, T. J., Grünwald, F., Kühn, J-P., Hoffmann, R-T., Kotzerke, J., Bethge, O., Schimmöller, L., Antoch, G., Müller, H-W., Daul, A., ... 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

@article{eb650c19bcfc404dabaf882312ce6682,

title = "Joint Imaging Platform for Federated Clinical Data Analytics",

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.",

author = "Jonas Scherer and Marco Nolden and Jens Kleesiek and Jasmin Metzger and Klaus Kades and Verena Schneider and Michael Bach and Oliver Sedlaczek and Bucher, {Andreas M} and Vogl, {Thomas J} and Frank Gr{\"u}nwald and Jens-Peter K{\"u}hn and Ralf-Thorsten Hoffmann and J{\"o}rg Kotzerke and Oliver Bethge and Lars Schimm{\"o}ller and Gerald Antoch and Hans-Wilhelm M{\"u}ller and Andreas Daul and Konstantin Nikolaou and {la Foug{\`e}re}, Christian and Kunz, {Wolfgang G} and Michael Ingrisch and Balthasar Schachtner and Jens Ricke and Peter Bartenstein and Felix Nensa and Alexander Radbruch and Lale Umutlu and Michael Forsting and Robert Seifert and Ken Herrmann and Philipp Mayer and Hans-Ulrich Kauczor and Tobias Penzkofer and Bernd Hamm and Winfried Brenner and Roman Kloeckner and Christoph D{\"u}ber and Mathias Schreckenberger and Rickmer Braren and Georgios Kaissis and Marcus Makowski and Matthias Eiber and Andrei Gafita and Rupert Trager and Weber, {Wolfgang A} and Jakob Neubauer and Marco Reisert and Michael Bock and Fabian Bamberg and J{\"u}rgen Hennig and Meyer, {Philipp Tobias} and Juri Ruf and Uwe Haberkorn and Schoenberg, {Stefan O} and Tristan Kuder and Peter Neher and Ralf Floca and Heinz-Peter Schlemmer and Klaus Maier-Hein",

year = "2020",

month = nov,

doi = "10.1200/CCI.20.00045",

language = "English",

volume = "4",

pages = "1027--1038",

journal = "JCO clinical cancer informatics",

issn = "2473-4276",

}

Scherer, J, Nolden, M, Kleesiek, J, Metzger, J, Kades, K, Schneider, V, Bach, M, Sedlaczek, O, Bucher, AM, Vogl, TJ, Grünwald, F, Kühn, J-P, Hoffmann, R-T, Kotzerke, J, Bethge, O, Schimmöller, L, Antoch, G, Müller, H-W, Daul, A, Nikolaou, K, la Fougère, 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, H-U, Penzkofer, T, Hamm, B, Brenner, W, Kloeckner, R, Düber, 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, H-P & Maier-Hein, K 2020, 'Joint Imaging Platform for Federated Clinical Data Analytics', JCO clinical cancer informatics, vol. 4, pp. 1027-1038. https://doi.org/10.1200/CCI.20.00045

TY - JOUR

T1 - Joint Imaging Platform for Federated Clinical Data Analytics

AU - Scherer, Jonas

AU - Nolden, Marco

AU - Kleesiek, Jens

AU - Metzger, Jasmin

AU - Kades, Klaus

AU - Schneider, Verena

AU - Bach, Michael

AU - Sedlaczek, Oliver

AU - Bucher, Andreas M

AU - Vogl, Thomas J

AU - Grünwald, Frank

AU - Kühn, Jens-Peter

AU - Hoffmann, Ralf-Thorsten

AU - Kotzerke, Jörg

AU - Bethge, Oliver

AU - Schimmöller, Lars

AU - Antoch, Gerald

AU - Müller, Hans-Wilhelm

AU - Daul, Andreas

AU - Nikolaou, Konstantin

AU - la Fougère, Christian

AU - Kunz, Wolfgang G

AU - Ingrisch, Michael

AU - Schachtner, Balthasar

AU - Ricke, Jens

AU - Bartenstein, Peter

AU - Nensa, Felix

AU - Radbruch, Alexander

AU - Umutlu, Lale

AU - Forsting, Michael

AU - Seifert, Robert

AU - Herrmann, Ken

AU - Mayer, Philipp

AU - Kauczor, Hans-Ulrich

AU - Penzkofer, Tobias

AU - Hamm, Bernd

AU - Brenner, Winfried

AU - Kloeckner, Roman

AU - Düber, Christoph

AU - Schreckenberger, Mathias

AU - Braren, Rickmer

AU - Kaissis, Georgios

AU - Makowski, Marcus

AU - Eiber, Matthias

AU - Gafita, Andrei

AU - Trager, Rupert

AU - Weber, Wolfgang A

AU - Neubauer, Jakob

AU - Reisert, Marco

AU - Bock, Michael

AU - Bamberg, Fabian

AU - Hennig, Jürgen

AU - Meyer, Philipp Tobias

AU - Ruf, Juri

AU - Haberkorn, Uwe

AU - Schoenberg, Stefan O

AU - Kuder, Tristan

AU - Neher, Peter

AU - Floca, Ralf

AU - Schlemmer, Heinz-Peter

AU - Maier-Hein, Klaus

PY - 2020/11

Y1 - 2020/11

N2 - 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.

AB - 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.

U2 - 10.1200/CCI.20.00045

DO - 10.1200/CCI.20.00045

M3 - Journal articles

C2 - 33166197

SN - 2473-4276

VL - 4

SP - 1027

EP - 1038

JO - JCO clinical cancer informatics

JF - JCO clinical cancer informatics

ER -