Application of an artificial intelligence-based tool in [
Artificial intelligence
Deep learning
Metabolic tumor volume (MTV)
Multiple myeloma
Objective quantification
Total lesion glycolysis (TLG)
[18F]FDG PET/CT
Journal
European journal of nuclear medicine and molecular imaging
ISSN: 1619-7089
Titre abrégé: Eur J Nucl Med Mol Imaging
Pays: Germany
ID NLM: 101140988
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
09
02
2023
accepted:
09
07
2023
medline:
23
10
2023
pubmed:
26
7
2023
entrez:
26
7
2023
Statut:
ppublish
Résumé
[ Whole-body [ BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [ The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.
Identifiants
pubmed: 37493665
doi: 10.1007/s00259-023-06339-5
pii: 10.1007/s00259-023-06339-5
pmc: PMC10547616
doi:
Substances chimiques
Fluorodeoxyglucose F18
0Z5B2CJX4D
Radiopharmaceuticals
0
Types de publication
Clinical Trial, Phase III
Journal Article
Multicenter Study
Randomized Controlled Trial
Langues
eng
Sous-ensembles de citation
IM
Pagination
3697-3708Informations de copyright
© 2023. The Author(s).
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