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Minerva Urologica e Nefrologica 2016 Aprile;68(2):138-49

lingua: Inglese

Update on novel imaging techniques for the detection of lymph node metastases in bladder cancer

Antti P. SALMINEN 1, Ivan JAMBOR 2, Kari T. SYVÄNEN 1, Peter J. BOSTRÖM 1

1 Department of Urology, University of Turku and Turku University Hospital, Turku, Finland; 2 Department of Diagnostic Radiology, University of Turku, Turku, Finland


Staging of muscle-invasive bladder cancer (MIBC) remains a challenge. It is generally acknowledged that the most commonly used imaging techniques have a trend to either upstage or downstage the disease. The aim of this review article is to evaluate the currently available scientific evidence for the use of imaging modalities in preoperative bladder cancer staging with special attention to the detection of lymph node metastasis (LNM). A non-systematic literature search utilizing PubMed database with terms MIBC and LN and MRI or PET or CT was performed with the search limited to articles published between 2010 and 2015. Magnetic resonance imaging (MRI) has shown potential for local tumor detection and staging in multiple studies, but the accuracy for LNM detection remains disappointingly low. The LN staging accuracy is improved with the use of ultra-small super-paramagnetic particles of iron oxide (USPIO). This experimental method, however, is not commercially available at the moment. Positron emission tomography (PET), a functional imaging technique most commonly accompanied with computed tomography (PET-CT), may also have a role in the detection of bladder cancer LNM in the future. According to the currently available scientific evidence, the following could be recommended for MIBC staging: 1. use of pelvic MRI for primary tumor evaluation and local LNM detection acknowledging limited nodal imaging accuracy; 2. pelvic/abdominal/chest CT for evaluation of distant metastasis. The scientific evidence does not support the routine use of PET-CT (18F-FDG, 18F/11C-choline, 11C-acetate) in bladder cancer staging or in LNM detection.

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