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dc.contributor.authorSkripcak, T
dc.contributor.authorBelka, C
dc.contributor.authorBosch, W
dc.contributor.authorBrink, C
dc.contributor.authorBrunner, T
dc.contributor.authorBudach, V
dc.contributor.authorBüttner, D
dc.contributor.authorDebus, J
dc.contributor.authorDekker, A
dc.contributor.authorGrau, C
dc.contributor.authorGulliford, S
dc.contributor.authorHurkmans, C
dc.contributor.authorJust, U
dc.contributor.authorKrause, M
dc.contributor.authorLambin, P
dc.contributor.authorLangendijk, J
dc.contributor.authorLewensohn, R
dc.contributor.authorLühr, A
dc.contributor.authorMaingon, P
dc.contributor.authorMasucci, M
dc.contributor.authorNiyazi, M
dc.contributor.authorPoortmans, P
dc.contributor.authorSimon, M
dc.contributor.authorSchmidberger, H
dc.contributor.authorSpezi, E
dc.contributor.authorStuschke, M
dc.contributor.authorValentini, V
dc.contributor.authorVerheij, M
dc.contributor.authorWhitfield, Gillian A
dc.contributor.authorZackrisson, B
dc.contributor.authorZips, D
dc.contributor.authorBaumann, M
dc.date.accessioned2015-01-22T15:29:44Z
dc.date.available2015-01-22T15:29:44Z
dc.date.issued2014-12
dc.identifier.citationCreating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets. 2014, 113 (3):303-309 Radiother Oncolen
dc.identifier.issn1879-0887
dc.identifier.pmid25458128
dc.identifier.doi10.1016/j.radonc.2014.10.001
dc.identifier.urihttp://hdl.handle.net/10541/338672
dc.description.abstractDisconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within "Big Data". Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology.
dc.languageENG
dc.language.isoenen
dc.rightsArchived with thanks to Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncologyen
dc.titleCreating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets.en
dc.typeArticleen
dc.contributor.departmentGerman Cancer Consortium (DKTK) Dresden and German Cancer Research Center (DKFZ) Heidelberg, Germanyen
dc.identifier.journalRadiotherapy and Oncologyen
html.description.abstractDisconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within "Big Data". Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology.


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