Abstract
PURPOSEImage 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.METHODSThe 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.RESULTSThe 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.CONCLUSIONThe 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.
Highlights
Medical imaging plays an essential role in most aspects of high-quality cancer care, from preventive measures, including screening and early detection, through diagnosis, treatment planning and monitoring, and follow-up
The Joint Imaging Platform (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
Summary
Medical imaging plays an essential role in most aspects of high-quality cancer care, from preventive measures, including screening and early detection, through diagnosis, treatment planning and monitoring, and follow-up. Most patients with cancer undergo repeated imaging during the course of their treatment. In combination with clinical and laboratory data, quantitative imaging biomarkers are of fundamental importance to improve standardized therapy monitoring in clinical multicenter studies.[1,2,3,4,5,6,7]. Medical images are more than pictures; they are data characterizing the patient.[8] As such, they are rightly subject to strict data protection, as well as ethical and moral requirements for scientific secondary use, which, in turn, can impede the exchange of biomedical imaging data across clinical sites. The clinical landscape is composed of heterogeneous information technology (IT) systems as well as different scanners and acquisition parameters, making joint projects and data
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