Radiomics in PET/CT: Current Status and Future AI-Based Evolutions
| Autor: | Dimitris Visvikis, V. Bourbonne, Florent Tixier, Ulrike Schick, François Lucia, Olena Tankyevych, Bogdan Badic, Nils Antonorsi, Vincent Jaouen, Mathieu Hatt, Catherine Cheze Le Rest |
|---|---|
| Přispěvatelé: | Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital de la Milétrie, Centre hospitalier universitaire de Poitiers (CHU Poitiers), Memorial Sloane Kettering Cancer Center [New York], IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), CCSD, Accord Elsevier, Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), IMT Atlantique (IMT Atlantique) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Diagnostic Imaging
media_common.quotation_subject Field (computer science) 030218 nuclear medicine & medical imaging Workflow 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Positron Emission Tomography Computed Tomography Medicine Humans Radiology Nuclear Medicine and imaging Quality (business) Segmentation ComputingMilieux_MISCELLANEOUS [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing media_common Pace Modalities business.industry Deep learning Reproducibility of Results Automation Data science 030220 oncology & carcinogenesis Artificial intelligence business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | Seminars in Nuclear Medicine Seminars in Nuclear Medicine, Elsevier, 2020, ⟨10.1053/j.semnuclmed.2020.09.002⟩ Seminars in Nuclear Medicine, 2020, ⟨10.1053/j.semnuclmed.2020.09.002⟩ |
| ISSN: | 0001-2998 |
| DOI: | 10.1053/j.semnuclmed.2020.09.002⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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