Precision medicine relies on molecular and systems biology methods as well as bidirectional association studies of phenotypes and (high-throughput) genomic data. However, the integrated use of such data often faces obstacles, especially in regards to data protection. An important prerequisite for research data processing is usually informed consent. But collecting consent is not always feasible, in particular when data are to be analyzed retrospectively. For phenotype data, anonymization, i.e. the altering of data in such a way that individuals cannot be identified, can provide an alternative. Several re-identification attacks have shown that this is a complex task and that simply removing directly identifying attributes such as names is usually not enough. More formal approaches are needed that use mathematical models to quantify risks and guide their reduction. Due to the complexity of these techniques, it is challenging and not advisable to implement them from scratch. Open software libraries and tools can provide a robust alternative. However, also the range of available anonymization tools is heterogeneous and obtaining an overview of their strengths and weaknesses is difficult due to the complexity of the problem space. We therefore performed a systematic review of open anonymization tools for structured phenotype data described in the literature between 1990 and 2021. Through a two-step eligibility assessment process, we selected 13 tools for an in-depth analysis. By comparing the supported anonymization techniques and further aspects, such as maturity, we derive recommendations for tools to use for anonymizing phenotype datasets with different properties.

Read the full articel: https://doi.org/10.1093/bib/bbac440

Autor:innen: Birte Lindstädt, Aliaksandra Shutsko, ZB MED – Information Centre Life Sciences, Cologne on behalf of the NFDI4Health Consortium and the NFDI4Health Task Force COVID-19 ; in close cooperation with: Martin Golebiewski, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg Dennis-Kenji Kipker, Vanessa Lettieri, University of Bremen, Bremen Sophie Klopfenstein, Carina Vorisek, Berlin Institute of Health at Charité (BIH), Berlin Matthias Löbe, University of Leipzig, Leipzig Carsten Oliver Schmidt, University of Greifswald, Greifswald 

DOI:  10.4126/FRL01-006431467

Autor:innen: Birte Lindstädt, Aliaksandra Shutsko, ZB MED – Information Centre Life Sciences, Cologne on behalf of the NFDI4Health Consortium and the NFDI4Health Task Force COVID-19 ; in close cooperation with: Martin Golebiewski, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg Dennis-Kenji Kipker, Vanessa Lettieri, University of Bremen, Bremen Sophie Klopfenstein, Carina Vorisek, Berlin Institute of Health at Charité (BIH), Berlin Matthias Löbe, University of Leipzig, Leipzig Carsten Oliver Schmidt, University of Greifswald, Greifswald 

Das entwickelte Bildanalyse-Werkzeug annotiert vollautomatisch Thorax-CTs von Covid-19 Patient:innen. Die Lungen, die Lungenlappen und die für COVID-19 typischen pathologischen Veränderungen in der Lunge werden segmentiert und das Volumen berechnet. Der CORADS-Score und der Severity Score zeigen die Wahrscheinlichkeit für COVID-19 und den Schweregrad der Krankheit an. Zudem bewertet eine Bildqualitätsanalyse, ob die Qualität des Bilddatensatzes ausreichend ist für die angebotenen Methoden.

Auf https://grand-challenge.org/reader-studies/satori-nfdi4health-test/ gibt es eine Demonstration des Bildanalyse-Werkzeugs mit einem Datensatz. Interessierte Nutzer:innen haben die Möglichkeit diese Bildanalyse mit eigenen CT-Studien-Datensätzen zu nutzen, um diese auszuwerten.

Autor:innen: Juliane Fluck, Birte Lindstädt, Wolfgang Ahrens, Oya Beyan, Benedikt Buchner , Johannes Darms, Ralf Depping, Jens Dierkes, Hubertus Neuhausen, Wolfgang Müller, Hajo Zeeb, Martin Golebiewski, Markus Löffler, Matthias Löbe, Frank Meineke, Sebastian Klammt, Holger Fröhlich, Horst Hahn, Matthias Schulze, Tobias Pischon, Ute Nöthlings, Ulrich Sax, Harald Kusch, Linus Grabenhenrich, Carsten Oliver Schmidt, Dagmar Waltemath, Sebastian Semler, Juliane Gehrke, Toralf Kirsten, Fabian Praßer, Sylvia Thun, Lothar Wieler, Iris Pigeot | Erschienen in: Bausteine Forschungsdatenmanagement Empfehlungen und Erfahrungsberichte für die Praxis von Forschungsdatenmanagerinnen und -managern. Nr.2/2021:S.72-85.

Autor:innen: Lisa Langnickel, Roman Baum, Johannes Darms, Sumit Madan, Juliane Fluck | Erschienen in: Stud Health Technol Inform. 2021 May 27;281:78-82.

Autor:innen: Carsten Oliver Schmidt, Johannes Darms, Aliaksandra Shutsko, Matthias Löbe, Rajini Nagrani, Bastian Seifert, Birte Lindstädt, Martin Golebiewski, Sofiya Koleva, Theresa Bender, Christian Robert Bauer, Ulrich Sax, Xiaoming Hu, Michael Lieser, Vivien Junker, Sophie Klopfenstein, Atinkut Zeleke, Dagmar Waltemath, Iris Pigeot, Juliane Fluck, NFDI4Health Task Force COVID-19 | Erschienen in: Stud Health Technol Inform. 2021 May 27;281:794-798.

PMID: 34042687

DOI: 10.3233/SHTI210284

Autor:innen: Carsten Oliver Schmidt, Juliane Fluck, Martin Golebiewski, Linus Grabenhenrich, Horst Hahn, Toralf Kirsten, Sebastian Klammt, Matthias Löbe, Ulrich Sax, Sylvia Thun, Iris Pigeot, and NFDI4Health Task Force Covid-19 | Erschienen in: Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2021; 64(9): 1084–1092.

DOI: https://doi.org/10.1007/s00103-021-03386-x