Literature DB >> 18635221

Validation of Partin tables and development of a preoperative nomogram for Japanese patients with clinically localized prostate cancer using 2005 International Society of Urological Pathology consensus on Gleason grading: data from the Clinicopathological Research Group for Localized Prostate Cancer.

Seiji Naito1, Kentaro Kuroiwa, Naoko Kinukawa, Ken Goto, Hirofumi Koga, Osamu Ogawa, Masaru Murai, Taizo Shiraishi.   

Abstract

PURPOSE: We validated the 2001 Partin tables and developed an original nomogram for Japanese patients using the 2005 International Society of Urological Pathology consensus on Gleason grading.
MATERIALS AND METHODS: Prostatectomy specimens from 1,188 Japanese men who underwent radical prostatectomy for clinically localized prostate cancer (cT1-2) between 1997 and 2005 were analyzed. Polychotomous logistic regression analysis was used to construct a nomogram to predict final pathological stage (organ confined disease, extraprostatic extension, seminal vesicle invasion and lymph node involvement) from 3 variables, including serum prostate specific antigen, clinical stage and biopsy Gleason score. The area under the ROC curve was used to compare the new nomogram with the Partin tables.
RESULTS: Preoperative serum prostate specific antigen and biopsy Gleason score were higher in the Japanese cohort than in the Partin cohort. The distribution of clinical and final pathological stages was similar in the 2 cohorts. The AUC for predicting organ confined disease was 0.699 and 0.717 for data applied to the Partin tables and to the new nomogram, respectively. The AUC for predicting lymph node involvement was 0.793 and 0.863, respectively.
CONCLUSIONS: To our knowledge this is the first preoperative nomogram developed for clinically localized prostate cancer in Japanese patients. Although the new nomogram predicted the pathological stage of prostate cancer in Japanese patients more accurately than the Partin tables, it did not satisfactorily predict organ confined disease. However, other predictive variables, such as more detailed pathological features of biopsy specimens or magnetic resonance imaging, may further improve prediction accuracy.

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Year:  2008        PMID: 18635221     DOI: 10.1016/j.juro.2008.05.047

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  22 in total

Review 1.  Controversies associated with the evaluation of elderly men with localized prostate cancer when considering radical prostatectomy.

Authors:  Koji Mitsuzuka; Yoichi Arai
Journal:  Int J Clin Oncol       Date:  2014-08-26       Impact factor: 3.402

2.  Clinical factors affecting perioperative outcomes in robot-assisted radical prostatectomy.

Authors:  Tomohiko Murakami; Satoshi Otsubo; Ryo Namitome; Masaki Shiota; Junichi Inokuchi; Ario Takeuchi; Eiji Kashiwagi; Katsunori Tatsugami; Masatoshi Eto
Journal:  Mol Clin Oncol       Date:  2018-09-13

3.  Comparison of the diagnostic efficacy and perioperative outcomes of limited versus extended pelvic lymphadenectomy during robot-assisted radical prostatectomy: a multi-institutional retrospective study in Japan.

Authors:  Shuichi Morizane; Masashi Honda; Satoshi Fukasawa; Atsushi Komaru; Junichi Inokuchi; Masatoshi Eto; Masaki Shimbo; Kazunori Hattori; Yoshiaki Kawano; Atsushi Takenaka
Journal:  Int J Clin Oncol       Date:  2017-12-11       Impact factor: 3.402

4.  Risk assessment among prostate cancer patients receiving primary androgen deprivation therapy.

Authors:  Matthew R Cooperberg; Shiro Hinotsu; Mikio Namiki; Kazuto Ito; Jeanette Broering; Peter R Carroll; Hideyuki Akaza
Journal:  J Clin Oncol       Date:  2009-08-10       Impact factor: 44.544

5.  Predicting prostate biopsy outcome: artificial neural networks and polychotomous regression are equivalent models.

Authors:  Nathan Lawrentschuk; Gina Lockwood; Peter Davies; Andy Evans; Joan Sweet; Ants Toi; Neil E Fleshner
Journal:  Int Urol Nephrol       Date:  2010-05-13       Impact factor: 2.370

6.  Risk-stratified survival rates and predictors of biochemical recurrence after radical prostatectomy in a Nara, Japan, cohort study.

Authors:  Nobumichi Tanaka; Kiyohide Fujimoto; Akihide Hirayama; Kazumasa Torimoto; Eijiro Okajima; Masahiro Tanaka; Makito Miyake; Keiji Shimada; Noboru Konishi; Yoshihiko Hirao
Journal:  Int J Clin Oncol       Date:  2011-03-25       Impact factor: 3.402

7.  Transperineal prostate brachytherapy, using I-125 seed with or without adjuvant androgen deprivation, in patients with intermediate-risk prostate cancer: study protocol for a phase III, multicenter, randomized, controlled trial.

Authors:  Kenta Miki; Takayoshi Kiba; Hiroshi Sasaki; Masahito Kido; Manabu Aoki; Hiroyuki Takahashi; Keiko Miyakoda; Takushi Dokiya; Hidetoshi Yamanaka; Masanori Fukushima; Shin Egawa
Journal:  BMC Cancer       Date:  2010-10-21       Impact factor: 4.430

8.  Outcomes and predictive factors of prostate cancer patients with extremely high prostate-specific antigen level.

Authors:  Kouji Izumi; Wen-Jye Lin; Hiroshi Miyamoto; Chiung-Kuei Huang; Aerken Maolake; Yasuhide Kitagawa; Yoshifumi Kadono; Hiroyuki Konaka; Atsushi Mizokami; Mikio Namiki
Journal:  J Cancer Res Clin Oncol       Date:  2014-04-19       Impact factor: 4.553

9.  An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011.

Authors:  John B Eifler; Zhaoyang Feng; Brian M Lin; Michael T Partin; Elizabeth B Humphreys; Misop Han; Jonathan I Epstein; Patrick C Walsh; Bruce J Trock; Alan W Partin
Journal:  BJU Int       Date:  2012-07-26       Impact factor: 5.588

Review 10.  The evolving Gleason grading system.

Authors:  Ni Chen; Qiao Zhou
Journal:  Chin J Cancer Res       Date:  2016-02       Impact factor: 5.087

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