001     157614
005     20240229133507.0
024 7 _ |a 10.2967/jnumed.120.246363
|2 doi
024 7 _ |a pmid:32817141
|2 pmid
024 7 _ |a 0022-3123
|2 ISSN
024 7 _ |a 0097-9058
|2 ISSN
024 7 _ |a 0161-5505
|2 ISSN
024 7 _ |a 1535-5667
|2 ISSN
024 7 _ |a 2159-662X
|2 ISSN
024 7 _ |a altmetric:88650022
|2 altmetric
037 _ _ |a DKFZ-2020-01711
041 _ _ |a eng
082 _ _ |a 610
100 1 _ |a Sprute, Katharina
|b 0
245 _ _ |a Diagnostic accuracy of 18F-PSMA-1007-PET/CT imaging for lymph node staging of prostate carcinoma in primary and biochemical recurrence.
260 _ _ |a New York, NY
|c 2021
|b Soc.
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1611560927_13973
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2021 Feb;62(2):208-213
520 _ _ |a Purpose: Prostate specific membrane antigen (PSMA) -ligand PET/CT is performed in patients with prostate cancer to stage the disease initially or to identify sites of recurrence after definitive therapy. 18F-PSMA-1007 is a promising PSMA-PET tracer based on clinical results, but detailed histologic confirmation has been lacking. Experimental Design: 96 patients with prostate cancer received a 18F-PSMA-1007-PET/CT followed by either radical prostatectomy with lymphadenectomy or salvage lymphadenectomy. The histological findings of PSMA-PET-positive nodes were analysed retrospectively. A lesion and a patient-based analysis was performed comparing 1) all positive and 2) only lesions with a size larger than 3 mm in histopathology. Results: 90.6% of the patients received 18F-PSMA-1007-PET/CT for staging before the primary treatment, while 9.4 % of the cohort underwent imaging for biochemical recurrence. In 34.4% of the cohort positive lymph nodes were present in imaging. A total of 1746 lymph nodes were dissected in 96 patients. 18F-PSMA-1007-PET had a lesion-based sensitivity of 81.7% a specificity of 99.6%, a positive predictive value (PPV) of 92.4%, a negative predictive value (NPV) of 98.9% for detecting positive lymph nodes larger than 3 mm. In the analysis of all malignant nodes regardless the size the overall sensitivity, specificity, PPV, and NPV on lesion-based analysis was 71.2%, 99.5%, 91.3%, and 97.9%, respectively. The patient-based analysis showed a sensitivity of 85.9% and a specificity of 99.5% for lymph nodes >3 mm. Conclusion:18F-PSMA-1007-PET/CT reliably detects malignant lymph nodes and has an exceptional specificity of >99% for nodal metastases.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|x 0
|f POF IV
588 _ _ |a Dataset connected to CrossRef, PubMed,
700 1 _ |a Kramer, Vasko
|b 1
700 1 _ |a Koerber, Stefan
|b 2
700 1 _ |a Meneses, Manuel
|b 3
700 1 _ |a Fernandez, Rene
|b 4
700 1 _ |a Soza-Ried, Cristian
|b 5
700 1 _ |a Eiber, Mathias
|b 6
700 1 _ |a Weber, Wolfgang
|b 7
700 1 _ |a Rauscher, Isabel
|b 8
700 1 _ |a Rahbar, Kambiz
|b 9
700 1 _ |a Schaefers, Martin
|b 10
700 1 _ |a Watabe, Tadashi
|b 11
700 1 _ |a Uemura, Motohide
|b 12
700 1 _ |a Naka, Sadahiro
|b 13
700 1 _ |a Nonomura, Norio
|b 14
700 1 _ |a Hatazawa, Jun
|b 15
700 1 _ |a Schwab, Constantin
|b 16
700 1 _ |a Schütz, Viktoria
|b 17
700 1 _ |a Hohenfellner, Markus
|b 18
700 1 _ |a Holland-Letz, Tim
|0 P:(DE-He78)457c042884c901eb0a02c18bb1d30103
|b 19
|u dkfz
700 1 _ |a Debus, Juergen
|b 20
700 1 _ |a Kratochwil, Clemens
|b 21
700 1 _ |a Amaral, Horacio
|b 22
700 1 _ |a Choyke, Peter L
|b 23
700 1 _ |a Haberkorn, Uwe
|b 24
700 1 _ |a Sandoval, Camilo
|b 25
700 1 _ |a Giesel, Frederik L
|b 26
773 _ _ |a 10.2967/jnumed.120.246363
|g p. jnumed.120.246363 -
|0 PERI:(DE-600)2040222-3
|n 2
|p 208-213
|t Journal of nuclear medicine
|v 62
|y 2021
|x 2159-662X
909 C O |o oai:inrepo02.dkfz.de:157614
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 19
|6 P:(DE-He78)457c042884c901eb0a02c18bb1d30103
913 0 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-313
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-300
|4 G:(DE-HGF)POF
|v Cancer risk factors and prevention
|x 0
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2021
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b J NUCL MED : 2018
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2020-01-18
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-01-18
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2020-01-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2020-01-18
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b J NUCL MED : 2018
|d 2020-01-18
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21