PMID- 18942119
OWN - NLM
STAT- MEDLINE
DCOM- 20090126
LR  - 20191210
IS  - 1097-0045 (Electronic)
IS  - 0270-4137 (Linking)
VI  - 69
IP  - 2
DP  - 2009 Feb 1
TI  - A [-2]proPSA-based artificial neural network significantly improves 
      differentiation between prostate cancer and benign prostatic diseases.
PG  - 198-207
LID - 10.1002/pros.20872 [doi]
AB  - BACKGROUND: The aim of this study was to combine the new automated Access 
      [-2]proPSA (p2PSA) assay with a percent free PSA (%fPSA) based artificial neural 
      network (ANN) or logistic regression (LR) model to enhance discrimination between 
      patients with prostate cancer (PCa) and with no evidence of malignancy (NEM) and 
      to detect aggressive PCa. METHODS: Sera from 311 PCa patients and 275 NEM 
      patients were measured with the p2PSA, total PSA (tPSA) and free PSA (fPSA) 
      assays on Access immunoassay technology (Beckman Coulter, Fullerton, CA) within 
      the 0-30 ng/ml tPSA range. Four hundred seventy-five patients (264 PCa, 211 NEM) 
      had a tPSA of 2-10 ng/ml. LR models and leave-one-out (LOO) ANN models with 
      Bayesian regularization by using tPSA, %fPSA, p2PSA/fPSA (%p2PSA), age and 
      prostate volume were constructed and compared by receiver-operating 
      characteristic (ROC) curve analysis. RESULTS: The ANN and LR model each utilizing 
      %p2PSA, %fPSA, tPSA and age, but without prostate volume, reached the highest 
      AUCs (0.85 and 0.84) and best specificities (ANN: 62.1% and 45.5%; LR: 53.1% and 
      41.2%) compared with tPSA (22.7% and 11.4%) and %fPSA (45.5% and 26.1%) at 90% 
      and 95% sensitivity. The %p2PSA furthermore distinguished better than tPSA and 
      %fPSA between pT2 and pT3, and Gleason sum <7 and >or=7 PCa. CONCLUSIONS: The 
      automated p2PSA assay offers a new tool to improve PCa detection, and especially 
      aggressive PCa detection. Incorporation of %p2PSA into an ANN and LR model 
      further enhances the diagnostic accuracy to differentiate between malignant and 
      non-malignant prostate diseases.
FAU - Stephan, Carsten
AU  - Stephan C
AD  - Department of Urology, Charite-Universitatsmedizin Berlin, Berlin, Germany. 
      carsten.stephan@charite.de
FAU - Kahrs, Anna-Maria
AU  - Kahrs AM
FAU - Cammann, Henning
AU  - Cammann H
FAU - Lein, Michael
AU  - Lein M
FAU - Schrader, Mark
AU  - Schrader M
FAU - Deger, Serdar
AU  - Deger S
FAU - Miller, Kurt
AU  - Miller K
FAU - Jung, Klaus
AU  - Jung K
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Prostate
JT  - The Prostate
JID - 8101368
RN  - EC 3.4.21.77 (Prostate-Specific Antigen)
SB  - IM
MH  - Aged
MH  - Automation/methods
MH  - Berlin
MH  - Humans
MH  - Immunoassay/methods
MH  - Male
MH  - Middle Aged
MH  - *Neural Networks, Computer
MH  - Prostate/anatomy & histology/pathology
MH  - Prostate-Specific Antigen/*analysis
MH  - Prostatic Diseases/diagnosis/*pathology
MH  - Prostatic Hyperplasia/pathology
MH  - Prostatic Neoplasms/diagnosis/*pathology
MH  - Regression Analysis
MH  - Sample Size
MH  - Sensitivity and Specificity
EDAT- 2008/10/23 09:00
MHDA- 2009/01/27 09:00
CRDT- 2008/10/23 09:00
PHST- 2008/10/23 09:00 [pubmed]
PHST- 2009/01/27 09:00 [medline]
PHST- 2008/10/23 09:00 [entrez]
AID - 10.1002/pros.20872 [doi]
PST - ppublish
SO  - Prostate. 2009 Feb 1;69(2):198-207. doi: 10.1002/pros.20872.