tranSMART-XNAT Connector tranSMART-XNAT connector—image selection based on clinical phenotypes and genetic profiles

He, Sijin; Yong, May; Matthews, Paul M.; Guo, Yike

doi:10.1093/bioinformatics/btw714

Cited by 3 publications

(2 citation statements)

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“…We demonstrated its performance by downloading and viewing a DICOM series from the web-based DICOM viewer. Related work projects also allow to download or view images, but usually not directly from a DWH query result table [24] or directly from the production PACS [20][21][22][23]. Our current webbased DICOM viewer is served with full DICOM files in order to provide interactive functionality to modify DICOM window levels.…”

Section: Discussionmentioning

confidence: 99%

Unlocking the PACS DICOM Domain for its Use in Clinical Research Data Warehouses

et al. 2020

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Clinical Data Warehouses (DWHs) are used to provide researchers with simplified access to pseudonymized and homogenized clinical routine data from multiple primary systems. Experience with the integration of imaging and metadata from picture archiving and communication systems (PACS), however, is rare. Our goal was therefore to analyze the viability of integrating a production PACS with a research DWH to enable DWH queries combining clinical and medical imaging metadata and to enable the DWH to display and download images ad hoc. We developed an application interface that enables to query the production PACS of a large hospital from a clinical research DWH containing pseudonymized data. We evaluated the performance of bulk extracting metadata from the PACS to the DWH and the performance of retrieving images ad hoc from the PACS for display and download within the DWH. We integrated the system into the query interface of our DWH and used it successfully in four use cases. The bulk extraction of imaging metadata required a median (quartiles) time of 0.09 (0.03-2.25) to 12.52 (4.11-37.30) seconds for a median (quartiles) number of 10 (3-29) to 103 (8-693) images per patient, depending on the extraction approach. The ad hoc image retrieval from the PACS required a median (quartiles) of 2.57 (2.57-2.79) seconds per image for the download, but 5.55 (4.91-6.06) seconds to display the first and 40. 77 (38.60-41.63) seconds to display all images using the pure web-based viewer. A full integration of a production PACS with a research DWH is viable and enables various use cases in research. While the extraction of basic metadata from all images can be done with reasonable effort, the extraction of all metadata seems to be more appropriate for subgroups.

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Section: Discussionmentioning

confidence: 99%

Unlocking the PACS DICOM Domain for its Use in Clinical Research Data Warehouses

et al. 2020

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“…Even though the harmonized DIC research data repository in Figure 2 looks like one singular data store, in reality it will be a set of data integration and data exploration repositories, which shall be used for dedicated purposes depending on the clinical/research scenario and the types of data which shall be integrated (e.g. i2b2 [ 17 , 18 ] and an OMOP DB [ 3 ] for clinical data, tranSMART for clinical and molecular/ge-nomic data [ 19 , 20 , 21 ], XNAT for imaging data [ 22 , 23 , 24 ]).…”

Section: Architectural Framework and Methodologymentioning

confidence: 99%

MIRACUM: Medical Informatics in Research and Care in University Medicine

Prokosch¹,

Acker²,

Bernarding³

et al. 2018

Methods Inf Med

114

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Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. Objectives: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. Governance and Policies: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. Architectural Framework and Methodology: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. Use Cases: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. Results: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. Discussion: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.

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