PMID- 18522632
OWN - NLM
STAT- MEDLINE
DCOM- 20081028
LR  - 20191210
IS  - 1464-410X (Electronic)
IS  - 1464-4096 (Linking)
VI  - 102
IP  - 7
DP  - 2008 Sep
TI  - An artificial neural network for five different assay systems of 
      prostate-specific antigen in prostate cancer diagnostics.
PG  - 799-805
LID - 10.1111/j.1464-410X.2008.07765.x [doi]
AB  - 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.
FAU - Stephan, Carsten
AU  - Stephan C
AD  - Institute for Medical Informatics, Charlite-Universitatsmedzin Berlin, Berlin, 
      Germany. carsten.stephan@charite.de
FAU - Cammann, Henning
AU  - Cammann H
FAU - Meyer, Hellmuth-Alexander
AU  - Meyer HA
FAU - Muller, Christian
AU  - Muller C
FAU - Deger, Serdar
AU  - Deger S
FAU - Lein, Michael
AU  - Lein M
FAU - Jung, Klaus
AU  - Jung K
LA  - eng
PT  - Comparative Study
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20080603
PL  - England
TA  - BJU Int
JT  - BJU international
JID - 100886721
RN  - EC 3.4.21.77 (Prostate-Specific Antigen)
SB  - IM
CIN - BJU Int. 2008 Sep;102(7):902; author reply 902. PMID: 18821923
MH  - Aged
MH  - Biological Assay/methods
MH  - Cohort Studies
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - *Neural Networks, Computer
MH  - Prostate-Specific Antigen/*metabolism
MH  - Prostatic Neoplasms/*diagnosis
MH  - Retrospective Studies
MH  - Sensitivity and Specificity
EDAT- 2008/06/05 09:00
MHDA- 2008/10/29 09:00
CRDT- 2008/06/05 09:00
PHST- 2008/06/05 09:00 [pubmed]
PHST- 2008/10/29 09:00 [medline]
PHST- 2008/06/05 09:00 [entrez]
AID - BJU7765 [pii]
AID - 10.1111/j.1464-410X.2008.07765.x [doi]
PST - ppublish
SO  - BJU Int. 2008 Sep;102(7):799-805. doi: 10.1111/j.1464-410X.2008.07765.x. Epub 
      2008 Jun 3.