Literature DB >> 23291910

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

Ping Tang1, Hui Chen, Matthew Uhlman, Yu-Rong Lin, Xiang-Rong Deng, Bin Wang, Wen-Jun Yang, Ke-Ji Xie.   

Abstract

Nomograms for predicting the risk of prostate cancer developed using other populations may introduce sizable bias when applied to a Chinese cohort. In the present study, we sought to develop a nomogram for predicting the probability of a positive initial prostate biopsy in a Chinese population. A total of 535 Chinese men who underwent a prostatic biopsy for the detection of prostate cancer in the past decade with complete biopsy data were included. Stepwise logistic regression was used to determine the independent predictors of a positive initial biopsy. Age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE) status, % free PSA and transrectal ultrasound (TRUS) findings were included in the analysis. A nomogram model was developed that was based on these independent predictors to calculate the probability of a positive initial prostate biopsy. A receiver-operating characteristic curve was used to assess the accuracy of using the nomogram and PSA levels alone for predicting positive prostate biopsy. The rate for positive initial prostate biopsy was 41.7% (223/535). The independent variables used to predict a positive initial prostate biopsy were age, PSA, PV and DRE status. The areas under the receiver-operating characteristic curve for a positive initial prostate biopsy for PSA alone and the nomogram were 79.7% and 84.8%, respectively. Our results indicate that the risk of a positive initial prostate biopsy can be predicted to a satisfactory level in a Chinese population using our nomogram. The nomogram can be used to identify and counsel patients who should consider a prostate biopsy, ultimately enhancing accuracy in diagnosing prostate cancer.

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Year:  2012        PMID: 23291910      PMCID: PMC3739110          DOI: 10.1038/aja.2012.111

Source DB:  PubMed          Journal:  Asian J Androl        ISSN: 1008-682X            Impact factor:   3.285


  22 in total

1.  Comparison of time trends in prostate cancer incidence (1973-2002) in Asia, from cancer incidence in five continents, Vols IV-IX.

Authors:  Tomohiro Matsuda; Kumiko Saika
Journal:  Jpn J Clin Oncol       Date:  2009-07       Impact factor: 3.019

2.  Prostate cancer prevention trial and European randomized study of screening for prostate cancer risk calculators: a performance comparison in a contemporary screened cohort.

Authors:  Vítor Cavadas; Luís Osório; Francisco Sabell; Frederico Teves; Frederico Branco; Miguel Silva-Ramos
Journal:  Eur Urol       Date:  2010-06-22       Impact factor: 20.096

3.  Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths.

Authors:  Rebecca Siegel; Elizabeth Ward; Otis Brawley; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2011-06-17       Impact factor: 508.702

4.  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

5.  Screening and prostate-cancer mortality in a randomized European study.

Authors:  Fritz H Schröder; Jonas Hugosson; Monique J Roobol; Teuvo L J Tammela; Stefano Ciatto; Vera Nelen; Maciej Kwiatkowski; Marcos Lujan; Hans Lilja; Marco Zappa; Louis J Denis; Franz Recker; Antonio Berenguer; Liisa Määttänen; Chris H Bangma; Gunnar Aus; Arnauld Villers; Xavier Rebillard; Theodorus van der Kwast; Bert G Blijenberg; Sue M Moss; Harry J de Koning; Anssi Auvinen
Journal:  N Engl J Med       Date:  2009-03-18       Impact factor: 91.245

6.  Mortality results from a randomized prostate-cancer screening trial.

Authors:  Gerald L Andriole; E David Crawford; Robert L Grubb; Saundra S Buys; David Chia; Timothy R Church; Mona N Fouad; Edward P Gelmann; Paul A Kvale; Douglas J Reding; Joel L Weissfeld; Lance A Yokochi; Barbara O'Brien; Jonathan D Clapp; Joshua M Rathmell; Thomas L Riley; Richard B Hayes; Barnett S Kramer; Grant Izmirlian; Anthony B Miller; Paul F Pinsky; Philip C Prorok; John K Gohagan; Christine D Berg
Journal:  N Engl J Med       Date:  2009-03-18       Impact factor: 91.245

7.  Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy.

Authors:  Satoru Kawakami; Noboru Numao; Yuhei Okubo; Fumitaka Koga; Shinya Yamamoto; Kazutaka Saito; Yasuhisa Fujii; Junji Yonese; Hitoshi Masuda; Kazunori Kihara; Iwao Fukui
Journal:  Eur Urol       Date:  2008-01-15       Impact factor: 20.096

8.  A comparative study of prostate cancer detection and management in China and in France.

Authors:  Eric Michaël Peyromaure; Kaili Mao; Yinghao Sun; Shujie Xia; Nin Jiang; Shiqing Zhang; Gongxian Wang; Zhongmin Liu; Bernard Debré
Journal:  Can J Urol       Date:  2009-02       Impact factor: 1.344

Review 9.  The comparability of models for predicting the risk of a positive prostate biopsy with prostate-specific antigen alone: a systematic review.

Authors:  Fritz Schröder; Michael W Kattan
Journal:  Eur Urol       Date:  2008-05-22       Impact factor: 20.096

10.  Predicting the outcome of prostate biopsy: comparison of a novel logistic regression-based model, the prostate cancer risk calculator, and prostate-specific antigen level alone.

Authors:  David J Hernandez; Misop Han; Elizabeth B Humphreys; Leslie A Mangold; Samir S Taneja; Stacy J Childs; Georg Bartsch; Alan W Partin
Journal:  BJU Int       Date:  2008-10-24       Impact factor: 5.588

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  20 in total

Review 1.  Prostate cancer nomograms: a review of their use in cancer detection and treatment.

Authors:  R J Caras; Joseph R Sterbis
Journal:  Curr Urol Rep       Date:  2014-03       Impact factor: 3.092

2.  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

3.  Prostate transitional zone volume-based nomogram for predicting prostate cancer and high progression prostate cancer in a real-world population.

Authors:  Yanqing Wang; Shaowei Xie; Xun Shangguan; Jiahua Pan; Yinjie Zhu; Zhixiang Xin; Fan Xu; Xiaoguang Shao; Liancheng Fan; Jianjun Sha; Qiang Liu; Baijun Dong; Wei Xue
Journal:  J Cancer Res Clin Oncol       Date:  2017-03-10       Impact factor: 4.553

4.  Reference Ranges of Age-Related Prostate-Specific Antigen in Men without Cancer from Beijing Area.

Authors:  Xin Liu; Jie Wang; Shun-Xin Zhang; Qian Lin
Journal:  Iran J Public Health       Date:  2013-11       Impact factor: 1.429

5.  Age-Specific Cutoff Value for the Application of Percent Free Prostate-Specific Antigen (PSA) in Chinese Men with Serum PSA Levels of 4.0-10.0 ng/ml.

Authors:  Rui Chen; Yiran Huang; Xiaobing Cai; Liping Xie; Dalin He; Liqun Zhou; Chuanliang Xu; Xu Gao; Shancheng Ren; Fubo Wang; Lulin Ma; Qiang Wei; Changjun Yin; Ye Tian; Zhongquan Sun; Qiang Fu; Qiang Ding; Junhua Zheng; Zhangqun Ye; Dingwei Ye; Danfeng Xu; Jianquan Hou; Kexin Xu; Jianlin Yuan; Xin Gao; Chunxiao Liu; Tiejun Pan; Yinghao Sun
Journal:  PLoS One       Date:  2015-06-19       Impact factor: 3.240

6.  A prostate biopsy strategy based on a new clinical nomogram reduces the number of biopsy cores required in high-risk patients.

Authors:  Yuan Huang; Gong Cheng; Bianjiang Liu; Pengfei Shao; Chao Qin; Jie Li; Lixin Hua; Changjun Yin
Journal:  BMC Urol       Date:  2014-01-11       Impact factor: 2.264

7.  Configuration and validation of a novel prostate disease nomogram predicting prostate biopsy outcome: A prospective study correlating clinical indicators among Filipino adult males with elevated PSA level.

Authors:  Michael E Chua; Patrick P Tanseco; Jonathan S Mendoza; Josefino C Castillo; Marcelino L Morales; Saturnino L Luna
Journal:  Asian J Urol       Date:  2015-04-21

8.  The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer.

Authors:  Yudong Cao; Min Cao; Yuke Chen; Wei Yu; Yu Fan; Qing Liu; Ge Gao; Zheng Zhao; Xiaoying Wang; Jie Jin
Journal:  Oncotarget       Date:  2017-07-04

9.  The prediction value of PI-RADS v2 score in high-grade Prostate Cancer: a multicenter retrospective study.

Authors:  Song Chen; Yun Yang; Tianchen Peng; Xi Yu; Haiqing Deng; Zhongqiang Guo
Journal:  Int J Med Sci       Date:  2020-05-30       Impact factor: 3.738

10.  A retrospective study of prostate cancer cases mimicking urothelial cell carcinoma of the bladder.

Authors:  Ranlu Liu; Xiaoqiang Xie; Zhihong Zhang; Yong Xu
Journal:  Eur J Med Res       Date:  2013-10-03       Impact factor: 2.175

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