Literature DB >> 16104903

Clinical utility of human glandular kallikrein 2 within a neural network for prostate cancer detection.

Carsten Stephan1, Klaus Jung, Antoninus Soosaipillai, George M Yousef, Henning Cammann, Hellmuth Meyer, Chuanliang Xu, Eleftherios P Diamandis.   

Abstract

OBJECTIVE: To assess, using artificial neural networks (ANNs), human glandular kallikrein 2 (hK2), prostate-specific antigen (PSA), and percentage free/total PSA (f/tPSA), for discriminating between prostate cancer and benign prostatic hyperplasia (BPH).
MATERIAL AND METHODS: Serum samples from 475 patients with prostate cancer (n = 347) or BPH (n = 128) within the PSA range of 1-20 ng/mL were analysed for tPSA, fPSA and hK2 (research assay, Toronto, Canada). Data were analysed in the ranges of 1-4, 2-4, 4-10, and 2-20 ng/mL tPSA. Back-propagation ANN models with the variables PSA, f/tPSA, and hK2, hK2/fPSA and hK2/(f/tPSA) were constructed. The diagnostic validity was evaluated by receiver-operating characteristic (ROC) curve analysis.
RESULTS: Whereas the median concentration of hK2 was not significantly different between patients with BPH or prostate cancer in any of the tPSA ranges, the f/tPSA, hK2/fPSA and hK2/(f/tPSA), and the hK2-based ANN outputs were always significantly different between patients with prostate cancer or BPH. Using ROC curve comparison, all variables were significantly better than hK2 in all ranges. The hK2-based ANN performed better than f/tPSA except in the 4-10 ng/mL tPSA range. At 90% and 95% sensitivity, the hK2-based ANN was also significantly better than f/tPSA in the 1-4 ng/mL tPSA range. hK2/(f/tPSA) achieved equal results to the hK2-based ANN except in the range 2-20 ng/mL tPSA.
CONCLUSIONS: The hK2-based ANN improves the outcome of f/tPSA but not hK2/(f/tPSA) in almost all analysed subgroups. When comparing the results at 90% and 95% sensitivity the hK2-based ANN only performed significantly better than f/tPSA in the lowest tPSA range. Only in lower tPSA ranges do hK2-based ANNs show an advantage for further improving prostate cancer detection.

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Year:  2005        PMID: 16104903     DOI: 10.1111/j.1464-410X.2005.05677.x

Source DB:  PubMed          Journal:  BJU Int        ISSN: 1464-4096            Impact factor:   5.588


  4 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.  Solid-phase nuclear magnetic resonance immunoassay for the prostate-specific antigen by using protein-coated magnetic nanoparticles.

Authors:  Pavel Khramtsov; Maria Kropaneva; Maria Bochkova; Valeria Timganova; Svetlana Zamorina; Mikhail Rayev
Journal:  Mikrochim Acta       Date:  2019-11-12       Impact factor: 5.833

4.  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

  4 in total

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