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)

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 neu...

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Published in:Insights into imaging Vol. 10; no. 1; pp. 87 - 16
Main Authors: deSouza, Nandita M., Achten, Eric, Alberich-Bayarri, Angel, Bamberg, Fabian, Boellaard, Ronald, Clément, Olivier, Fournier, Laure, Gallagher, Ferdia, Golay, Xavier, Heussel, Claus Peter, Jackson, Edward F., Manniesing, Rashindra, Mayerhofer, Marius E., Neri, Emanuele, O’Connor, James, Oguz, Kader Karli, Persson, Anders, Smits, Marion, van Beek, Edwin J. R., Zech, Christoph J.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 29.08.2019
Springer Nature B.V
Springer
SpringerOpen
Subjects:
Artificial intelligence
Biomarkers
Cardiovascular disease
Clinical decision making
Computer Science
Coronary artery disease
Decision making
Diagnostic Radiology
Image detection
Imaging
Imaging biomarkers
Internal Medicine
Interventional Radiology
Machine learning
Medical Imaging
Medicine
Medicine & Public Health
Neuroradiology
Object recognition
Pattern recognition
Quantitation
Radiology
Standardisation
Statement
Ultrasound
Standardisation
Clinical decision making
Quantitation
Imaging biomarkers
ISSN:1869-4101, 1869-4101
Online Access:Get full text
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Summary: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.
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ISSN:1869-4101
1869-4101
DOI:10.1186/s13244-019-0764-0