• CookGary J RGJRCancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK. Electronic address: gary.cook@kcl and Vicky Goh.
• Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK. Electronic address: gary.cook@kcl.ac.uk.
• Semin Nucl Med. 2020 Nov 1; 50 (6): 532-540.

AbstractRadiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.Copyright © 2020 Elsevier Inc. All rights reserved.

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