Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positro...
Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography
About this item
Full title
Author / Creator
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
To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal
18
F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) and biochemical recurrence (BCR).
Methods
A total of 251 consecutive
18
F-fluc...
Alternative Titles
Full title
Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_2415295513
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2415295513
Other Identifiers
ISSN
1619-7070
E-ISSN
1619-7089
DOI
10.1007/s00259-020-04912-w
