• Abdalla Ibrahim, Martin Vallières, Henry Woodruff, Sergey Primakov, Mohsen Beheshti, Simon Keek, Turkey Refaee, Sebastian Sanduleanu, Sean Walsh, Olivier Morin, Philippe Lambin, Roland Hustinx, and Felix M Mottaghy.
• The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
• Semin Nucl Med. 2019 Sep 1; 49 (5): 438-449.

AbstractRadiomics - the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

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