Literature DB >> 11992419

An artificial neural network considerably improves the diagnostic power of percent free prostate-specific antigen in prostate cancer diagnosis: results of a 5-year investigation.

Carsten Stephan1, Klaus Jung, Henning Cammann, Birgit Vogel, Brigitte Brux, Glen Kristiansen, Birgit Rudolph, Steffen Hauptmann, Michael Lein, Dietmar Schnorr, Pranav Sinha, Stefan A Loening.   

Abstract

Our study was performed to evaluate the diagnostic usefulness of %fPSA alone and combined with an ANN at different PSA concentration ranges, including the low range 2-4 ng/ml, to improve the risk assessment of prostate cancer. A total of 928 men with prostate cancer and BPH without any pretreatment of the prostate in the PSA range 2-20 ng/ml were enrolled in the study between 1996 and 2001. An ANN with input data of PSA, %fPSA, patient's age, prostate volume and DRE status was developed to calculate the individual's risk before performing a prostate biopsy within the different PSA ranges 2-4, 4.1-10 and 10.1-20 ng/ml. ROC analysis and cut-off calculations were used to estimate the diagnostic improvement of %fPSA and ANN in comparison to PSA. At the 90% sensitivity level, %fPSA and ANN performed better than PSA in all ranges, enhancing the specificity by 15-28% and 32-44%, respectively. For the low PSA range 2-4 ng/mL, we recommend a first-time biopsy at an ANN specificity level of 90%. For PSA 4-10 ng/mL, we recommend a first-time biopsy based on the ANN at the 90% sensitivity level. Use of an ANN enhances the %fPSA performance to further reduce the number of unnecessary biopsies within the PSA range 2-10 ng/ml. Copyright 2002 Wiley-Liss, Inc.

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Year:  2002        PMID: 11992419     DOI: 10.1002/ijc.10370

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  11 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

2.  The value of an artificial neural network in the decision-making for prostate biopsies.

Authors:  R P Meijer; E F A Gemen; I E W van Onna; J C van der Linden; H P Beerlage; G C M Kusters
Journal:  World J Urol       Date:  2009-06-28       Impact factor: 4.226

Review 3.  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

Review 4.  [Value of biomarkers in urology].

Authors:  P J Goebell; B Keck; S Wach; B Wullich
Journal:  Urologe A       Date:  2010-04       Impact factor: 0.639

5.  Assay-specific artificial neural networks for five different PSA assays and populations with PSA 2-10 ng/ml in 4,480 men.

Authors:  Carsten Stephan; Chuanliang Xu; Henning Cammann; Markus Graefen; Alexander Haese; Hartwig Huland; Axel Semjonow; Eleftherios P Diamandis; Mesut Remzi; Bob Djavan; Mark F Wildhagen; Bert G Blijenberg; Patrik Finne; Ulf-Hakan Stenman; Klaus Jung; Hellmuth-Alexander Meyer
Journal:  World J Urol       Date:  2007-02-28       Impact factor: 4.226

6.  The Mount Sinai Prebiopsy Risk Calculator for Predicting any Prostate Cancer and Clinically Significant Prostate Cancer: Development of a Risk Predictive Tool and Validation with Advanced Neural Networking, Prostate Magnetic Resonance Imaging Outcome Database, and European Randomized Study of Screening for Prostate Cancer Risk Calculator.

Authors:  Sneha Parekh; Parita Ratnani; Ugo Falagario; Dara Lundon; Deepshikha Kewlani; Jordan Nasri; Zach Dovey; Dimitrios Stroumbakis; Daniel Ranti; Ralph Grauer; Stanislaw Sobotka; Adriana Pedraza; Vinayak Wagaskar; Lajja Mistry; Ivan Jambor; Anna Lantz; Otto Ettala; Armando Stabile; Pekka Taimen; Hannu J Aronen; Juha Knaapila; Ileana Montoya Perez; Giorgio Gandaglia; Alberto Martini; Wolfgang Picker; Erik Haug; Luigi Cormio; Tobias Nordström; Alberto Briganti; Peter J Boström; Giuseppe Carrieri; Kenneth Haines; Michael A Gorin; Peter Wiklund; Mani Menon; Ash Tewari
Journal:  Eur Urol Open Sci       Date:  2022-05-20

7.  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

8.  [An artificial neural network as a tool in risk evaluation of prostate cancer. Indication for biopsy with the PSA range of 2-20 microg/l].

Authors:  C Stephan; B Vogel; H Cammann; M Lein; V Klevecka; P Sinha; G Kristiansen; D Schnorr; K Jung; S A Loening
Journal:  Urologe A       Date:  2003-03-22       Impact factor: 0.639

9.  The use of an artificial neural network in the evaluation of the extracorporeal shockwave lithotripsy as a treatment of choice for urinary lithiasis.

Authors:  Athanasios Tsitsiflis; Yiannis Kiouvrekis; Georgios Chasiotis; Georgios Perifanos; Stavros Gravas; Ioannis Stefanidis; Vassilios Tzortzis; Anastasios Karatzas
Journal:  Asian J Urol       Date:  2021-09-30

10.  External validation of an artificial neural network and two nomograms for prostate cancer detection.

Authors:  Thorsten H Ecke; Steffen Hallmann; Stefan Koch; Jürgen Ruttloff; Henning Cammann; Holger Gerullis; Kurt Miller; Carsten Stephan
Journal:  ISRN Urol       Date:  2012-07-05
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