Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity

Carsten Stephan¹ , Nicola Büker¹ , Henning Cammann² , Hellmuth-Alexander Meyer¹ , Michael Lein¹^,3 and Klaus Jung¹^,3

¹Department of Urology, Charité – Universitätsmedizin Berlin, Germany

²Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Germany

³Berlin Institute for Urologic Research, Germany

author email corresponding author email

BMC Urology 2008, 8:10doi:10.1186/1471-2490-8-10


Published:	2 September 2008

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.

Conclusion

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.