The value of an artificial neural network in the decision-making for prostate biopsies

Purpose: In majority of patients who are subjected to prostate biopsies, no prostate cancer (PCa) is found. It is important to prevent unnecessary biopsies since serious complications may occur. An artificial neural network (ANN) may be able to predict the risk of the presence of PCa.

Methods: Included were all patients, who underwent transrectal ultrasound-guided prostate biopsies between June 2006 and June 2007 with a total PSA (tPSA) level between 2 and 20 μg/l. The patients were divided into two groups according to their tPSA level (2–10 μg/l and 10–20 μg/l). The ANN Prostataclass of the Universitätsklinikum Charité in Berlin was used. The predictions of the ANN were compared to the pathology results of the biopsies.

Results: Overall 165 patients were included. PCa was diagnosed in 53 patients, whereas the ANN predicted “no risk” in 19 of these patients (36%). The ANN output receiver operator characteristic (ROC) plots for the range of tPSA 2–10 μg/l and tPSA 10–20 μg/l showed an area under the curve (AUC) of 63 and 88% for the initial biopsy group, versus 69 and 57%, respectively, for the repeat biopsy group.

Conclusions: he ANN resulted in a false negative rate of 36%, missing PCa in 19 patients. For use in an outpatient-clinical setting, this ANN is insufficient to predict the risk of presence of PCa reliably.