Literature DB >> 16697520

A (-5, -7) proPSA based artificial neural network to detect prostate cancer.

Carsten Stephan1, Hellmuth-Alexander Meyer, Maciej Kwiatkowski, Franz Recker, Henning Cammann, Stefan A Loening, Klaus Jung, Michael Lein.   

Abstract

OBJECTIVE: The pro-forms of prostate specific antigen (-2,-5,-7 proPSA) and also %free PSA based artificial neural networks (ANN) have been suggested to enhance the discrimination between prostate cancer (PCa) and no evidence of malignancy (NEM). This study reports on the combined use of proPSA within a %free PSA based ANN to enhance specificity of PCa.
METHODS: Serum samples from 898 patients with PCa (n=384) or NEM (n=514) within the PSA range 1-10 microg/l were analyzed for PSA, free PSA and (-5,-7) proPSA (Roche assays). Patient data from two centers - taken first from the Swiss site of the ERSPC (Aarau) and from a referral population (Berlin) have been analyzed. Leave-one-out ANN models with the variables PSA, %fPSA, proPSA, prostate volume and status of digital rectal examination (DRE) were constructed and compared by receiver-operating characteristic (ROC) curve analysis.
RESULTS: (-5,-7) proPSA was only significantly different between NEM and PCa in the PSA range 4-10 microg/l. Within the PSA range 4-10 microg/l (Berlin group) the ANN including only the two variables %fPSA and proPSA could reach the same performance like the conventional ANN with PSA, %fPSA, age, prostate volume and DRE (both AUCs: 0.84) However, at 95% sensitivity all ANN could not improve specificity compared to %fPSA.
CONCLUSIONS: ProPSA as single parameter did not improve specificity over %fPSA whereas proPSA and %fPSA within an ANN in the PSA range 4-10 microg/l substituted prostate volume and DRE. At 95% sensitivity only ANN with prostate volume and DRE perform significantly better than %fPSA.

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Year:  2006        PMID: 16697520     DOI: 10.1016/j.eururo.2006.04.011

Source DB:  PubMed          Journal:  Eur Urol        ISSN: 0302-2838            Impact factor:   20.096


  10 in total

1.  [New serum markers in prostate carcinoma and their application to artificial neural networks].

Authors:  C Stephan; K Jung; H Cammann; J Kramer; G Kristiansen; S A Loening; M Lein
Journal:  Urologe A       Date:  2007-09       Impact factor: 0.639

Review 2.  Artificial neural networks and prostate cancer--tools for diagnosis and management.

Authors:  Xinhai Hu; Henning Cammann; Hellmuth-A Meyer; Kurt Miller; Klaus Jung; Carsten Stephan
Journal:  Nat Rev Urol       Date:  2013-02-12       Impact factor: 14.432

3.  A prospective, multicenter, National Cancer Institute Early Detection Research Network study of [-2]proPSA: improving prostate cancer detection and correlating with cancer aggressiveness.

Authors:  Lori J Sokoll; Martin G Sanda; Ziding Feng; Jacob Kagan; Isaac A Mizrahi; Dennis L Broyles; Alan W Partin; Sudhir Srivastava; Ian M Thompson; John T Wei; Zhen Zhang; Daniel W Chan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-05       Impact factor: 4.254

4.  Use of artificial neural networks in the management of antenatally diagnosed ureteropelvic junction obstruction.

Authors:  Ilker Seçkiner; Serap Ulusam Seçkiner; Omer Bayrak; Sakıp Erturhan
Journal:  Can Urol Assoc J       Date:  2011-03-01       Impact factor: 1.862

5.  Diagnostic significance of [-2]pro-PSA and prostate dimension-adjusted PSA-related indices in men with total PSA in the 2.0-10.0 ng/mL range.

Authors:  Kazuto Ito; Mai Miyakubo; Yoshitaka Sekine; Hidekazu Koike; Hiroshi Matsui; Yasuhiro Shibata; Kazuhiro Suzuki
Journal:  World J Urol       Date:  2012-08-18       Impact factor: 4.226

6.  [-2]proenzyme prostate specific antigen for prostate cancer detection: a national cancer institute early detection research network validation study.

Authors:  Lori J Sokoll; Yinghui Wang; Ziding Feng; Jacob Kagan; Alan W Partin; Martin G Sanda; Ian M Thompson; Daniel W Chan
Journal:  J Urol       Date:  2008-06-11       Impact factor: 7.450

7.  The establishment and evaluation of a new model for the prediction of prostate cancer.

Authors:  Qi Wang; Yan-Feng Li; Jun Jiang; Yong Zhang; Xu-Dong Liu; Ke Li
Journal:  Medicine (Baltimore)       Date:  2017-03       Impact factor: 1.889

8.  Machine learning methods can more efficiently predict prostate cancer compared with prostate-specific antigen density and prostate-specific antigen velocity.

Authors:  Satoshi Nitta; Masakazu Tsutsumi; Shotaro Sakka; Tsuyoshi Endo; Kenichiro Hashimoto; Morikuni Hasegawa; Takayuki Hayashi; Koji Kawai; Hiroyuki Nishiyama
Journal:  Prostate Int       Date:  2019-01-29

9.  A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

Authors:  Hesham Salem; Daniele Soria; Jonathan N Lund; Amir Awwad
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-22       Impact factor: 2.796

10.  Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity.

Authors:  Carsten Stephan; Nicola Büker; Henning Cammann; Hellmuth-Alexander Meyer; Michael Lein; Klaus Jung
Journal:  BMC Urol       Date:  2008-09-02       Impact factor: 2.264

  10 in total

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