Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity
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.
Author: Carsten Stephan, Nicola Buker, Henning Cammann, Hellmuth-Alexander Meyer, Michael Lein and Klaus Jung
Credits/Source: BMC Urology 2008, 8:10
Published on: 2008-09-02
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