Literature DB >> 20363164

Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine.

Thorsten H Ecke1, Peter Bartel, Steffen Hallmann, Stefan Koch, Jürgen Ruttloff, Henning Cammann, Michael Lein, Mark Schrader, Kurt Miller, Carsten Stephan.   

Abstract

BACKGROUND: We evaluated the use of the artificial neural network (ANN) program "ProstataClass" of the Department of Urology and the Institute of Medical Informatics at the Charité-Universitätsmedizin Berlin in daily routine to increase prostate cancer (CaP) detection rate and to reduce unnecessary biopsies.
MATERIALS AND METHODS: From May 2005 to April 2007, a total of 204 patients were included in the study. The Beckman Access PSA assay was used, and pretreatment prostate specific antigen (PSA) was measured prior to digital rectal examination (DRE) and 12 core systematic transrectal ultrasound (TRUS) guided biopsies. The individual ANN predictions were generated with the use of the ANN application for the Beckman Access PSA and free PSA assays, which relies on age, PSA, percent free prostate specific antigen (%fPSA), prostate volume, and DRE. Diagnostic validity of total prostate specific antigen (tPSA), %fPSA, and the ANN was evaluated by ROC curve analysis.
RESULTS: PSA and %fPSA ranged from 4.01 to 9.91 ng/ml (median: 6.65) and 5% to 48% (median: 15%), respectively. Of all men, 46 (22.5%) demonstrated suspicious DRE findings. Total prostate volume ranged from 7.1 to 119.2 cc (median: 35). Overall, 71 (34.8%) CaP were detected. Of men with suspicious DRE, 28 (60.9%) had CaP on initial biopsy. The ANN was 78% accurate in the original report. The AUC of ROC curve analysis was 0.51 for PSA, 0.66 for %PSA, and 0.72 for the ANN-Output, respectively.
CONCLUSIONS: Our results in this independent cohort show that ANN is a very helpful parameter in daily routine to increase the CaP detection rate and reduce unnecessary biopsies. Copyright Â
© 2012 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20363164     DOI: 10.1016/j.urolonc.2009.12.009

Source DB:  PubMed          Journal:  Urol Oncol        ISSN: 1078-1439            Impact factor:   3.498


  3 in total

Review 1.  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

2.  Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging.

Authors:  Lukasz Matulewicz; Jacobus F A Jansen; Louisa Bokacheva; Hebert Alberto Vargas; Oguz Akin; Samson W Fine; Amita Shukla-Dave; James A Eastham; Hedvig Hricak; Jason A Koutcher; Kristen L Zakian
Journal:  J Magn Reson Imaging       Date:  2013-11-15       Impact factor: 4.813

3.  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

  3 in total

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