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An artificial neural network for five different assay systems of prostate‐specific antigen in prostate cancer diagnostics
Author(s) -
Stephan Carsten,
Cammann Henning,
Meyer HellmuthAlexander,
Müller Christian,
Deger Serdar,
Lein Michael,
Jung Klaus
Publication year - 2008
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/j.1464-410x.2008.07765.x
Subject(s) - prostate cancer , urology , medicine , prostate specific antigen , prostate , rectal examination , receiver operating characteristic , gynecology , oncology , cancer
OBJECTIVE To compare separate prostate‐specific antigen (PSA) assay‐specific artificial neural networks (ANN) for discrimination between patients with prostate cancer (PCa) and no evidence of malignancy (NEM). PATIENTS AND METHODS In 780 patients (455 with PCa, 325 with NEM) we measured total PSA (tPSA) and free PSA (fPSA) with five different assays: from Abbott (AxSYM), Beckman Coulter (Access), DPC (Immulite 2000), and Roche (Elecsys 2010) and with tPSA and complexed PSA (cPSA) assays from Bayer (ADVIA Centaur). ANN models were developed with five input factors: tPSA, percentage free/total PSA (%fPSA), age, prostate volume and digital rectal examination status for each assay separately to examine two tPSA ranges of 0–10 and 10–27 ng/mL. RESULTS Compared with the median tPSA concentrations (range from 4.9 [Bayer] to 6.11 ng/mL [DPC]) and especially the median %fPSA values (range from 11.2 [DPC] to 17.4%[Abbott], for tPSA 0–10 ng/mL), the areas under the receiver operating characteristic curves (AUC) for all calculated ANN models did not significantly differ from each other. The AUC were: 0.894 (Abbott), 0.89 (Bayer), 0.895 (Beckman), 0.882 (DPC) and 0.892 (Roche). At 95% sensitivity the specificities were without significant differences, whereas the individual absolute ANN outputs differed markedly. CONCLUSIONS Despite only slight differences, PSA assay‐specific ANN models should be used to optimize the ANN outcome to reduce the number of unnecessary prostate biopsies. We further developed the ANN named ‘ProstataClass’ to provide clinicians with an easy to use tool in making their decision about follow‐up testing.