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Cancer Imaging

Open Access

The value of 68Ga-PSMA enhanced PET-CT and MR-PET in patients with biochemical recurrent prostate cancer

  • E Rummeny1Email author,
  • K Holzapel1,
  • T Maurer1,
  • G Weirich1,
  • E Gschwend1 and
  • M Eiber1
Cancer Imaging201515(Suppl 1):P39

Published: 2 October 2015


Prostate CancerCancer ResearchNuclear MedicinePoint ScaleLymphnode Involvement

Aim of study

In patients with prostate cancer increased levels of PSMA can be measured. Recently a new tracer, 68Ga-PSMA, was developed as a specific marker for hybrid imaging (PET/CT, MR-PET). In this study we evaluated the accuracy of 68Ga-PSMA in patients with rising PSA after radical prosatectomy, so called “biochemical recurrent prostate cancer” (BRPC).

Materials and methods

A total of 322 patients with BRPC underwent either a PET-CT or a MR-PET examination (Siemens Biograph mMR) after injection of about 150 mBq68Ga-PSMA. Images were evaluated in cosensus by one experienced nuclear medicine physician and one radiologist. Pelvine lymphnode dissection was performed in most of the patients according to a predefined template with 8 fields. Lymphnode involvement was evaluated according to a 5 point scale with a patient- and a field-based analysis. These findings were stratified according to PSA-values.


Four patients were excluded from the study for different reasons. Sensitivity for detection of recurrence was 95.7 % for PSA-values ≥ 2ng/ml, 81.4 % for PSA-values of 1-2 ng/ml, 76% for PSA-values 0.5-1 ng/ml, and 51% for PSA values ≤ 0.5 ng/ml. In comparison to the MR-images alone MR-PET was of superior diagnostic value.


MR-PET using 68Ga-PSMA is a sensitive and highly accurate technique for the diagnosis of biochemical reccurence of prostate cancer after radical prostatectomy. It yields high diagnostic performance at relatively low PCA-values.

Authors’ Affiliations

Klinikum rechts der Isar, Technical University Munich, Munich, Germany


© Rummeny et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.