JaLCDOI 10.18926/AMO/52142
FullText URL 68_1_35.pdf
Author Araki, Motoo| Jeong, Wooju| Park, Sung Yul| Lee, Young Hoon| Nasu, Yasutomo| Kumon, Hiromi| Hong, Sung Joon| Rha, Koon Ho|
Abstract The purpose of this study was to compare the positive surgical margin (PSM) rates of 2 techniques of robot-assisted radical prostatectomy (RARP) for pT2 (localized) prostate cancer. A retrospective analysis was conducted of 361 RARP cases, performed from May 2005 to September 2008 by a single surgeon (KHR) at our institution (Yonsei University College of Medicine). In the conventional technique, the bladder neck was transected first. In the modified ultradissection, the lateral border of the bladder neck was dissected and then the bladder neck was transected while the detrusor muscle of the bladder was well visualized. Perioperative characteristics and outcomes and PSM rates were analyzed retrospectively for pT2 patients (n=217), focusing on a comparison of those undergoing conventional (n=113) and modified ultradissection (n=104) techniques. There was no difference between the conventional and modified ultradissection group in mean age, BMI, PSA, prostate volume, biopsy Gleason score, and DʼAmico prognostic criteria distributions. The mean operative time was shorter (p<0.001) and the estimated blood loss was less (p<0.01) in the modified ultradissection group. The PSM rate for the bladder neck was significantly reduced by modified ultradissection, from 6.2% to 0% (p<0.05). In conclusion, modified ultradissection reduces the PSM rate for the bladder neck.
Keywords robot-assisted radical prostatectomy prostate cancer surgery surgical margin technique
Amo Type Original Article
Published Date 2014-02
Publication Title Acta Medica Okayama
Volume volume68
Issue issue1
Publisher Okayama University Medical School
Start Page 35
End Page 41
ISSN 0386-300X
NCID AA00508441
Content Type Journal Article
language 英語
Copyright Holders CopyrightⒸ 2014 by Okayama University Medical School
File Version publisher
Refereed True
PubMed ID 24553487
Web of Science KeyUT 000331592800006