Mask R-CNN assisted 2.5D object detection pipeline of
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 01 2023
30 01 2023
Historique:
received:
02
07
2022
accepted:
23
01
2023
entrez:
30
1
2023
pubmed:
31
1
2023
medline:
2
2
2023
Statut:
epublish
Résumé
Prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) is a molecular and functional imaging modality with better restaging accuracy over conventional imaging for detecting prostate cancer in men suspected of lymph node (LN) progression after definitive therapy. However, the availability of PSMA PET/CT is limited in both low-resource settings and for repeating imaging surveillance. In contrast, CT is widely available, cost-effective, and routinely performed as part of patient follow-up or radiotherapy workflow. Compared with the molecular activities, the morphological and texture changes of subclinical LNs in CT are subtle, making manual detection of positive LNs infeasible. Instead, we harness the power of artificial intelligence for automated LN detection on CT. We examined
Identifiants
pubmed: 36717727
doi: 10.1038/s41598-023-28669-y
pii: 10.1038/s41598-023-28669-y
pmc: PMC9886937
doi:
Substances chimiques
gallium 68 PSMA-11
0
Gallium Radioisotopes
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1696Subventions
Organisme : NCI NIH HHS
ID : R01CA259008
Pays : United States
Informations de copyright
© 2023. The Author(s).
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