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Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.
Floca, Ralf; Bohn, Jonas; Haux, Christian; Wiestler, Benedikt; Zöllner, Frank G; Reinke, Annika; Weiß, Jakob; Nolden, Marco; Albert, Steffen; Persigehl, Thorsten; Norajitra, Tobias; Baeßler, Bettina; Dewey, Marc; Braren, Rickmer; Büchert, Martin; Fallenberg, Eva Maria; Galldiks, Norbert; Gerken, Annika; Götz, Michael; Hahn, Horst K; Haubold, Johannes; Haueise, Tobias; Große Hokamp, Nils; Ingrisch, Michael; Iuga, Andra-Iza; Janoschke, Marco; Jung, Matthias; Kiefer, Lena Sophie; Lohmann, Philipp; Machann, Jürgen; Moltz, Jan Hendrik; Nattenmüller, Johanna; Nonnenmacher, Tobias; Oerther, Benedict; Othman, Ahmed E; Peisen, Felix; Schick, Fritz; Umutlu, Lale; Wichtmann, Barbara D; Zhao, Wenzhao; Caspers, Svenja; Schlemmer, Heinz-Peter; Schlett, Christopher L; Maier-Hein, Klaus; Bamberg, Fabian.
Afiliación
  • Floca R; German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany. r.floca@dkfz-heidelberg.de.
  • Bohn J; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany. r.floca@dkfz-heidelberg.de.
  • Haux C; National Center for Radiation Research in Oncology NCRO, Heidelberg Institute for Radiation Oncology HIRO, Heidelberg, Germany. r.floca@dkfz-heidelberg.de.
  • Wiestler B; German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany.
  • Zöllner FG; Faculty of Bioscience, University of Heidelberg, Heidelberg, Germany.
  • Reinke A; National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
  • Weiß J; Translational Lung Research Center (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.
  • Nolden M; Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
  • Albert S; Department of Neuroradiology, TU Munich University Hospital, Munich, Germany.
  • Persigehl T; TranslaTUM - Central Institute for Translational Cancer Research, TU Munich, Munich, Germany.
  • Norajitra T; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
  • Baeßler B; Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
  • Dewey M; Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Braren R; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Büchert M; Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany.
  • Fallenberg EM; German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany.
  • Galldiks N; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
  • Gerken A; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
  • Götz M; Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
  • Hahn HK; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany.
  • Haubold J; German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany.
  • Haueise T; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
  • Große Hokamp N; Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
  • Ingrisch M; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin Institute of Health, DZHK (German Centre for Cardiovascular Research), and DKTK (German Cancer Consortium), both partner sites Berlin, Berlin, German
  • Iuga AI; Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany.
  • Janoschke M; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
  • Jung M; German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany.
  • Kiefer LS; Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany.
  • Lohmann P; Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany.
  • Machann J; Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
  • Moltz JH; Institute of Neuroscience and Medicine (INM-3), Research Center Juelich (FZJ), Juelich, Germany.
  • Nattenmüller J; Center of Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Bonn, Cologne & Duesseldorf, Germany.
  • Nonnenmacher T; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
  • Oerther B; Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany.
  • Othman AE; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
  • Peisen F; Faculty 3, Mathematics and Computer Science, University of Bremen, Bremen, Germany.
  • Schick F; Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
  • Umutlu L; Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany.
  • Wichtmann BD; Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany.
  • Zhao W; German Center for Diabetes Research (DZD), Tübingen, Germany.
  • Caspers S; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany.
  • Schlemmer HP; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Schlett CL; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany.
  • Maier-Hein K; Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany.
  • Bamberg F; Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany.
Insights Imaging ; 15(1): 124, 2024 Jun 03.
Article en En | MEDLINE | ID: mdl-38825600
ABSTRACT

OBJECTIVES:

Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND

METHODS:

The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges.

RESULTS:

Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows.

CONCLUSION:

To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Insights Imaging Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Insights Imaging Año: 2024 Tipo del documento: Article