title: Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity.
abstract: BACKGROUND
  To validate an artificial neural network (ANN) based on the combination of PSA velocity (PSAV) with a %free PSA-based ANN to enhance the discrimination between prostate cancer (PCa) and benign prostate hyperplasia (BPH).
  
  
  METHODS
  The study comprised 199 patients with PCa (n = 49) or BPH (n = 150) with at least three PSA estimations and a minimum of three months intervals between the measurements. Patients were classified into three categories according to PSAV and ANN velocity (ANNV) calculated with the %free based ANN "ProstataClass". Group 1 includes the increasing PSA and ANN values, Group 2 the stable values, and Group 3 the decreasing values.
  
  
  RESULTS
  71% of PCa patients typically have an increasing PSAV. In comparison, the ANNV only shows this in 45% of all PCa patients. However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65%) and increasing values are less frequently (11% vs. 21%) if the ANNV is used instead of the PSAV.
  
  
  CONCLUSIONS
  PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV. The ANNV cannot improve the PCa detection rate but may save 11-17% of unnecessary prostate biopsies in known BPH patients.
authors.affiliation: Department of Urology, Charité - Universitätsmedizin Berlin, Germany. carsten.stephan@charite.de
authors.forename: Carsten
authors.initials: C
authors.lastname: Stephan
authors.forename: Nicola
authors.initials: N
authors.lastname: Büker
authors.forename: Henning
authors.initials: H
authors.lastname: Cammann
authors.forename: Hellmuth-Alexander
authors.initials: HA
authors.lastname: Meyer
authors.forename: Michael
authors.initials: M
authors.lastname: Lein
authors.forename: Klaus
authors.initials: K
authors.lastname: Jung