Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study.
Aged
Automation
Choline
/ administration & dosage
Feasibility Studies
Fluorine Radioisotopes
/ administration & dosage
Humans
Image Interpretation, Computer-Assisted
/ methods
Male
Middle Aged
Neural Networks, Computer
Positron Emission Tomography Computed Tomography
Predictive Value of Tests
Prostate
/ diagnostic imaging
Prostatectomy
Prostatic Neoplasms
/ diagnostic imaging
Radiopharmaceuticals
/ administration & dosage
Workflow
agreement
choline
convolutional neural network
diagnostic imaging
positron emission tomography
prostatic neoplasms
Journal
Clinical physiology and functional imaging
ISSN: 1475-097X
Titre abrégé: Clin Physiol Funct Imaging
Pays: England
ID NLM: 101137604
Informations de publication
Date de publication:
Nov 2019
Nov 2019
Historique:
received:
05
03
2019
accepted:
12
08
2019
pubmed:
23
8
2019
medline:
1
4
2020
entrez:
23
8
2019
Statut:
ppublish
Résumé
To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). A convolutional neural network (CNN) was trained for automated measurements in The mean (range) weight of the prostate specimens was 44 g (20-109), while CNN-estimated volume was 62 ml (31-108) with a mean difference of 13·5 g or ml (95% CI: 9·78-17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Vol Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.
Substances chimiques
Fluorine Radioisotopes
0
Radiopharmaceuticals
0
Choline
N91BDP6H0X
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
399-406Subventions
Organisme : Danish Cancer Society
Organisme : Region of Southern Denmark
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2019 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.
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