68ga-prostate-specific membrane antigen-positron emission tomography/computed tomography in advanced prostate cancer: current state and future trends

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ID: 246776
2017
The early and accurate detection of prostate cancer is important to ensure timely management and appropriate individualized treatment. Currently, conventional imaging has limitations particularly in the early detection of metastases and at prostate-specific antigen (PSA) levels < 2.0 ng/mL. Furthermore, disease management such as salvage radiotherapy is best at low PSA levels. Thus, it is critical to capture the disease in the oligometastatic stage as disease progression and commencement of systemic therapies can be delayed by metastasis-directed therapy. Prostate-specific membrane antigen (PSMA) is overexpressed in prostatic cancer cells. Novel imaging modalities using radiolabeled tracers with PSMA such as 68Ga-PSMA-positron emission tomography (PET)/computed tomography (CT) have shown promising results. We review the literature regarding 68Ga-PSMA-PET/CT in the setting of primary prostate cancer and biochemical recurrence. At present, the best utilization of 68Ga-PSMA-PET/CT appears to be in biochemical recurrence. 68Ga-PSMA-PET/CT has high diagnostic accuracy for lymph node metastases and has been shown to have superior detection rates to conventional imaging, especially at low PSA levels. The exact role of 68Ga-PSMA-PET/CT in primary prostate cancer is not yet entirely clear. It has an improved detection rate for smaller lesions and may be able to identify nodal or distant metastatic disease at an earlier stage. While still experimental, there may also be value in combining 68Ga-PSMA-PET to multiparametric magnetic resonance imaging for staging of intraprostatic disease. To date, 68Ga-PSMA-PET/CT has been shown to have considerable clinical value and to impact treatment selection for patients with prostate cancer. Still in its infancy, the results of future clinical trials will be excitedly awaited.
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udovicich2017prostate68ga-prostate-specific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Cristian Udovicich;Marlon Perera;Michael S. Hofman;Shankar Siva;Andres Del Rio;Declan G. Murphy;Nathan Lawrentschuk
Journal
Year 2017
DOI 10.1016/j.prnil.2017.02.003
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