Radiomics in PET/CT: Current status and future AI-based evolutions Review


Authors: Hatt, M.; Cheze Le Rest, C.; Antonorsi, N.; Tixier, F.; Tankyevych, O.; Jaouen, V.; Lucia, F.; Bourbonne, V.; Schick, U.; Badic, B.; Visvikis, D.
Review Title: Radiomics in PET/CT: Current status and future AI-based evolutions
Abstract: This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of radiomics applied to the field of PET/CT imaging. Numerous pitfalls have been identified in study design, data acquisition, segmentation, features calculation and modeling by the radiomics community, and these are often the same issues across all image modalities and clinical applications, however some of these are specific to PET/CT (and SPECT/CT) imaging and therefore the present paper focuses on those. In most cases, recommendations and potential methodological solutions do exist and should therefore be followed to improve the overall quality and reproducibility of published studies. In terms of future evolutions, the techniques from the larger field of artificial intelligence (AI), including those relying on deep neural networks (also known as deep learning) have already shown impressive potential to provide solutions, especially in terms of automation, but also to maybe fully replace the tools the radiomics community has been using until now in order to build the usual radiomics workflow. Some important challenges remain to be addressed before the full impact of AI may be realized but overall the field has made striking advances over the last few years and it is expected advances will continue at a rapid pace. © 2020
Keywords: review; reproducibility; automation; artificial intelligence; calculation; workflow; human; deep learning; radiomics; positron emission tomography-computed tomography; single photon emission computed tomography-computed tomography; deep neural network
Journal Title: Seminars in Nuclear Medicine
Volume: 51
Issue: 2
ISSN: 0001-2998
Publisher: Elsevier Inc.  
Date Published: 2021-03-01
Start Page: 126
End Page: 133
Language: English
DOI: 10.1053/j.semnuclmed.2020.09.002
PUBMED: 33509369
PROVIDER: scopus
DOI/URL:
Notes: Review -- Export Date: 1 March 2021 -- Source: Scopus
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  1. Florent Tixier
    11 Tixier