OBJECTIVES: To evaluate the effect of previous prostate surgery performed for lower urinary tract symptoms (LUTS) on the ability to predict lymph node invasion (LNI) in patients subsequently diagnosed with prostate cancer, testing two widely used LNI predictive models.
SUBJECT / PATIENTS AND METHODS: From 1990 to 2012, we collected data on 4734 prostate cancer patients treated with radical prostatectomy and extended pelvic lymph node dissection. Of these, 4453 (94%) had no prior prostate surgery ("naïve patients"), while 286 (6%) had previously undergone surgery for LUTS. Two LNI prediction models based on patients treated with extended pelvic lymph node dissection were evaluated using the area under the receiver operating characteristics (ROC) curve, the calibration plot method, and decision curve analyses.
RESULTS: The rate of LNI was 12%, while the median number of lymph nodes removed was 15 in both groups (p=0.9). The two tested nomograms provided more accurate prediction in naïve patients relative to patients previously treated with prostate surgery for LUTS (AUC: 82% and 81% vs. 68% and 71%, p=0.01 and p=0.04 respectively). In naïve patients the surgeon would have missed one LNI for every 53 and 34 avoided ePLND using the Briganti and Godoy nomograms, respectively; in patients previously treated with surgery for LUTS, a LNI would have been missed in 13 and 21 patients not undergoing ePLND.
CONCLUSION: The accuracy and the clinical net-benefit of LNI prediction tools decrease importantly in patients with prior prostate surgery for LUTS. These models should be avoided in such patients, who should instead be subject to routine pelvic lymph node dissection.
Written by:
Fossati N, Sjoberg DD, Capitanio U, Gandaglia G, Larcher A, Nini A, Mirone V, Vickers AJ, Montorsi F, Briganti A. Are you the author?
Division of Oncology / Unit of Urology; URI; IRCCS Ospedale San Raffaele, Milan, Italy; Dept. of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
Reference: BJU Int. 2014 Aug 28. Epub ahead of print.
doi: 10.1111/bju.12912
PubMed Abstract
PMID: 25168453