Introduction to Radiomics.
PET
artificial intelligence
machine learning
radiomics
single-photon emission tomography
Journal
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
ISSN: 1535-5667
Titre abrégé: J Nucl Med
Pays: United States
ID NLM: 0217410
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
22
08
2019
accepted:
28
01
2020
pubmed:
16
2
2020
medline:
29
9
2020
entrez:
16
2
2020
Statut:
ppublish
Résumé
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.
Identifiants
pubmed: 32060219
pii: jnumed.118.222893
doi: 10.2967/jnumed.118.222893
pmc: PMC9374044
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
488-495Subventions
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Informations de copyright
© 2020 by the Society of Nuclear Medicine and Molecular Imaging.
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