Literature DB >> 16413348

Development of a nomogram to predict probability of positive initial prostate biopsy among Japanese patients.

Hiroyoshi Suzuki1, Akira Komiya, Naoto Kamiya, Takashi Imamoto, Koji Kawamura, Junichiro Miura, Noriyuki Suzuki, Hiroomi Nakatsu, Akira Hata, Tomohiko Ichikawa.   

Abstract

OBJECTIVES: Several nomograms for prostate cancer detection have recently been developed. Because the incidence of prostate cancer is lower among Asian men, nomograms based on Western populations cannot be directly applied to Japanese men. We, therefore, developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Japanese male population.
METHODS: Data were collected from 834 Japanese male referrals who underwent initial prostate biopsies as individual screening. We analyzed age, total prostate-specific antigen (PSA) level, free/total PSA (f/t PSA) ratio, prostate volume, and digital rectal examination findings. Of these data, we randomly reserved 20% for study validation. Logistic regression analysis estimated relative risk, 95% confidence intervals, and P values.
RESULTS: Independent predictors of a positive biopsy result included elevated PSA levels, decreased f/T PSA ratio, advanced age, small prostate volume, and abnormal digital rectal examination findings. We developed a predictive nomogram for an initial positive biopsy using these variables. The area under the receiver operating characteristic curve for the model was 81.8%, which was significantly greater than that of the prediction based on PSA alone (area under the receiver operating characteristic curve 67.8%). If externally validated, applying this model could reduce unnecessary biopsy procedures by 32% and reduce the overall need for prostate biopsies by 26%.
CONCLUSIONS: In this study of a Japanese population, incorporating clinical and laboratory data into a prebiopsy nomogram significantly improved the prediction of prostate cancer compared with predictions based solely on the individual factors.

Entities:  

Mesh:

Year:  2006        PMID: 16413348     DOI: 10.1016/j.urology.2005.07.040

Source DB:  PubMed          Journal:  Urology        ISSN: 0090-4295            Impact factor:   2.649


  21 in total

Review 1.  Role of nomograms for prostate cancer in 2007.

Authors:  Felix K-H Chun; Pierre I Karakiewicz; Hartwig Huland; Markus Graefen
Journal:  World J Urol       Date:  2007-02-27       Impact factor: 4.226

2.  Using biopsy to detect prostate cancer.

Authors:  Shahrokh F Shariat; Claus G Roehrborn
Journal:  Rev Urol       Date:  2008

3.  Chinese nomogram to predict probability of positive initial prostate biopsy: a study in Taiwan region.

Authors:  Shu-Chun Kuo; Shun-Hsing Hung; Hsien-Yi Wang; Chih-Chiang Chien; Chin-Li Lu; Hung-Jung Lin; How-Ran Guo; Jian-Fang Zou; Chian-Shiung Lin; Chien-Cheng Huang
Journal:  Asian J Androl       Date:  2013-10-14       Impact factor: 3.285

4.  Pre-operative prediction of advanced prostatic cancer using clinical decision support systems: accuracy comparison between support vector machine and artificial neural network.

Authors:  Sang Youn Kim; Sung Kyoung Moon; Dae Chul Jung; Sung Il Hwang; Chang Kyu Sung; Jeong Yeon Cho; Seung Hyup Kim; Jiwon Lee; Hak Jong Lee
Journal:  Korean J Radiol       Date:  2011-08-24       Impact factor: 3.500

5.  A nomogram based on age, prostate-specific antigen level, prostate volume and digital rectal examination for predicting risk of prostate cancer.

Authors:  Ping Tang; Hui Chen; Matthew Uhlman; Yu-Rong Lin; Xiang-Rong Deng; Bin Wang; Wen-Jun Yang; Ke-Ji Xie
Journal:  Asian J Androl       Date:  2012-12-10       Impact factor: 3.285

6.  Initial biopsy outcome prediction in Korean patients-comparison of a noble web-based Korean prostate cancer risk calculator versus prostate-specific antigen testing.

Authors:  Jae Young Park; Sungroh Yoon; Man Sik Park; Dae-Yeon Cho; Hong-Seok Park; Du Geon Moon; Duck Ki Yoon
Journal:  J Korean Med Sci       Date:  2010-12-22       Impact factor: 2.153

Review 7.  Critical review of prostate cancer predictive tools.

Authors:  Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino
Journal:  Future Oncol       Date:  2009-12       Impact factor: 3.404

8.  Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine.

Authors:  Hak Jong Lee; Sung Il Hwang; Seok-Min Han; Seong Ho Park; Seung Hyup Kim; Jeong Yeon Cho; Chang Gyu Seong; Gheeyoung Choe
Journal:  Eur Radiol       Date:  2009-12-17       Impact factor: 5.315

9.  A study of PSA values in an unselected sample of Senegalese men.

Authors:  Mohamed Jalloh; Charnita Zeigler-Johnson; Marguette Sylla-Niang; Lamine Niang; Issa Labou; Karamo A Konte; Timothy R Rebbeck; Serigne Gueye
Journal:  Can J Urol       Date:  2008-02       Impact factor: 1.344

10.  Serum thymidine kinase 1 is associated with Gleason score of patients with prostate carcinoma.

Authors:  Shujing Li; Jianping Zhou; Yu Wang; Keqin Zhang; Junjie Yang; Xinling Zhang; Chunmei Wang; Hongbo Ma; Ji Zhou; Ellen He; Sven Skog
Journal:  Oncol Lett       Date:  2018-08-21       Impact factor: 2.967

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.