Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer...
Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI
About this item
Full title
Author / Creator
Papp, L. , Spielvogel, C. P. , Grubmüller, B. , Grahovac, M. , Krajnc, D. , Ecsedi, B. , Sareshgi, R. A.M. , Mohamad, D. , Hamboeck, M. , Rausch, I. , Mitterhauser, M. , Wadsak, W. , Haug, A. R. , Kenner, L. , Mazal, P. , Susani, M. , Hartenbach, S. , Baltzer, P. , Helbich, T. H. , Kramer, G. , Shariat, S.F. , Beyer, T. , Hartenbach, M. and Hacker, M.
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Purpose
Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo...
Alternative Titles
Full title
Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI
Authors, Artists and Contributors
Author / Creator
Spielvogel, C. P.
Grubmüller, B.
Grahovac, M.
Krajnc, D.
Ecsedi, B.
Sareshgi, R. A.M.
Mohamad, D.
Hamboeck, M.
Rausch, I.
Mitterhauser, M.
Wadsak, W.
Haug, A. R.
Kenner, L.
Mazal, P.
Susani, M.
Hartenbach, S.
Baltzer, P.
Helbich, T. H.
Kramer, G.
Shariat, S.F.
Beyer, T.
Hartenbach, M.
Hacker, M.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8113201
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8113201
Other Identifiers
ISSN
1619-7070
E-ISSN
1619-7089
DOI
10.1007/s00259-020-05140-y
