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Title:

Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning.

Document type:
Journal Article
Author(s):
Capobianco, Nicolò; Sibille, Ludovic; Chantadisai, Maythinee; Gafita, Andrei; Langbein, Thomas; Platsch, Guenther; Solari, Esteban Lucas; Shah, Vijay; Spottiswoode, Bruce; Eiber, Matthias; Weber, Wolfgang A; Navab, Nassir; Nekolla, Stephan G
Abstract:
PURPOSE: In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based stagi...     »
Journal title abbreviation:
Eur J Nucl Med Mol Imaging
Year:
2022
Journal volume:
49
Journal issue:
2
Pages contribution:
517-526
Fulltext / DOI:
doi:10.1007/s00259-021-05473-2
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34232350
Print-ISSN:
1619-7070
TUM Institution:
Klinik und Poliklinik für Nuklearmedizin
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