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Insights Imaging. 2019 Aug 29;10(1):87. doi: 10.1186/s13244-019-0764-0.

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR).

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Cancer Research UK Imaging Centre, The Institute of Cancer Research and The Royal Marsden Hospital, Downs Road, Sutton, Surrey, SM2 5PT, UK.
Ghent University Hospital, Ghent, Belgium.
QUIBIM SL / La Fe Health Research Institute, Valencia, Spain.
Department of Radiology, University of Freiburg, Freiburg im Breisgau, Germany.
VU University Medical Center, Amsterdam, The Netherlands.
Hopital Européen Georges Pompidou, Paris, France.
University of Cambridge, Cambridge, UK.
UCL Institute of Neurology, London, UK.
Universitätsklinik Heidelberg, Translational Lung Research Center (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.
University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525, GA, Nijmegen, The Netherlands.
Medical University Vienna, Vienna, Austria.
Department of Translational Research, University of Pisa, Pisa, Italy.
Division of Cancer Sciences, University of Manchester, Manchester, UK.
Hacettepe University Hospitals, Ankara, Turkey.
Linköpings Universitet, Linköping, Sweden.
Department of Radiology and Nuclear Medicine (Ne-515), Erasmus MC, PO Box 2040, 3000, CA, Rotterdam, The Netherlands.
Edinburgh Imaging, Queen's Medical Research Institute, Edinburgh Bioquarter, 47 Little France Crescent, Edinburgh, UK.
University Hospital Basel, Radiology and Nuclear Medicine, University of Basel, Petersgraben 4, CH-4031, Basel, Switzerland.


Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.


Clinical decision making; Imaging biomarkers; Quantitation; Standardisation

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