Dual Energy Computed Tomography in Head and Neck Imaging: Pushing the Envelope.
Deep neural networks
Dual energy CT
Head and neck squamous cell carcinoma
Machine learning
Radiomics
Spectral CT
Texture analysis
Thyroid cartilage invasion
Journal
Neuroimaging clinics of North America
ISSN: 1557-9867
Titre abrégé: Neuroimaging Clin N Am
Pays: United States
ID NLM: 9211377
Informations de publication
Date de publication:
Aug 2020
Aug 2020
Historique:
entrez:
1
7
2020
pubmed:
1
7
2020
medline:
27
4
2021
Statut:
ppublish
Résumé
Multiple applications of dual energy computed tomography (DECT) have been described for the evaluation of disorders in the head and neck, especially in oncology. We review the body of evidence suggesting advantages of DECT for the evaluation of the neck compared with conventional single energy computed tomography scans, but the full potential of DECT is still to be realized. There is early evidence suggesting significant advantages of DECT for the extraction of quantitative biomarkers using radiomics and machine learning, representing a new horizon that may enable this technology to reach its full potential.
Identifiants
pubmed: 32600633
pii: S1052-5149(20)30026-5
doi: 10.1016/j.nic.2020.04.003
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
311-323Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Disclosure R. Forghani is a clinical research scholar (chercheur-boursier clinicien) supported by the FRQS (Fonds de recherche en santé du Québec). R. Forghani has acted as a consultant and speaker and has a research agreement and research support from GE Healthcare. R. Forghani is also founder and stockholder of 4intelligent Inc.