4.6 Article

Magnetic resonance imaging in the prediction of biochemical recurrence of prostate cancer after radical prostatectomy

Journal

BJU INTERNATIONAL
Volume 104, Issue 3, Pages 315-320

Publisher

WILEY
DOI: 10.1111/j.1464-410X.2009.08406.x

Keywords

prostate cancer; biochemical recurrence; magnetic resonance imaging; nomograms

Funding

  1. National Institutes of Health [R01 CA76423]
  2. Max Kade Foundation, New York

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OBJECTIVE To investigate whether magnetic resonance imaging (MRI) findings, when converted into a scoring system, can predict the biochemical recurrence of prostate cancer after radical prostatectomy (RP). PATIENTS AND METHODS Between January 2000 and October 2004, 610 patients with biopsy-confirmed prostate cancer had MRI before RP, with whole-mount step-sectioning of the pathology sample. MRI findings were retrospectively scored on a seven-point scale based on Tumour-Node-Mestastasis staging (1, no tumour seen, to 7, lymph node metastasis). MRI scores were added to published 5- and 10-year clinical preoperative nomograms for predicting recurrence. The predictive accuracy of MRI was quantified as the differences in bootstrap-corrected concordance indices of the models with and without MRI. RESULTS As of August 2007, 64 (10.5%) patients had a biochemical recurrence. MRI scores were associated with recurrence (P < 0.001) with hazard ratios of 1.76 and 1.81 in the 5- and 10-year models, respectively. Actual recurrence rates by MRI score were: 1, 0%; 2, 4.5%; 3, 9%; 4, 24.1%; 5, 33.3%; 6, 69.2%; 7, 100%. When MRI was added, the concordance indices of the 5- and 10-year models increased, from 0.762 to 0.776 (P = 0.081) and 0.773 to 0.788 (P = 0.107), respectively; the improvement was not significant. CONCLUSION The MRI scoring system devised was a strong predictor of biochemical recurrence after RP. Although MRI did not provide added prognostic value to standard clinical nomograms, in centres where MRI is used routinely, it might increase the confidence of the clinician in assessing the risk of recurrence by contributing supporting data.

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