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Management of PSA Recurrence After Definitive Therapy for Prostate Cancer

  • Ilia S. Zeltser
  • Richard K. Valicenti
  • Leonard G. Gomella
Chapter
  • 138 Downloads
Part of the Current Clinical Urology book series (CCU)

Abstract

The field of prostate cancer has witnessed dramatic improvements in the management of localized prostate cancer over the last 20 yr. It is thought that stage migration, probably because of screening efforts, combined with improvements in treatment modalities, is largely responsible for these outcomes. The initiation of prostate-specific antigen (PSA) screening has led not only to earlier detection of prostate cancer but also to detection at a lower stage of disease (1). Many more patients are now candidates for a definitive local therapy. However, there are patients with localized prostate cancer treated with curative intent who will have either rising PSA levels or clinical progression and who will require a secondary intervention (2). It is estimated that approx 134,000 patients receive localized disease treatment annually, and of those as many as 50,000 may experience PSA recurrence (3). Biochemical failure is defined as a detectable PSA level after definitive local therapy without clinical evidence of local or distal recurrence and is the most common pattern of disease progression. However, there is a subset of patients with biochemical recurrence after radical prostatectomy who on long-term follow-up do not exhibit PSA or clinical progression (4). Shinghal et al. (4) identified 14 patients (8.8% of biochemical recurrences) with a detectable PSA level after radical prostatectomy, yet without clinical or PSA progression at a mean follow-up of 10.3 yr. They described a benign clinical course in these patients, obviating the need for adjuvant therapy.

Keywords

Prostate Cancer Radical Prostatectomy Gleason Score Radiat Oncol Biol Phys Localize Prostate Cancer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Ilia S. Zeltser
  • Richard K. Valicenti
  • Leonard G. Gomella

There are no affiliations available

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