A Dynamic Model for Predicting Prostate Cancer in Iranian Men Based on a Perceptron Neural Network

Objectives: To test a novel neural network prediction model for prostate cancer based on age, rectal examination, prostate specific antigen (PSA) and prostate volume. Methods: 572 men who underwent trans-rectal ultrasound guided prostate biopsy from February, 2013 to September, 2014 participated in the study. Prostate configuration based on digital rectal examination, serum PSA level, and prostate volume were recorded. Pathologic outcomes were categorized in two groups: adenocarcinoma vs. noncancerous reports. A multi-layer perceptron (MLP) neural network was designed in which total PSA, free PSA, age, rectal examination results and prostate volume were vectors. Results: 566 men with the average age of 65.9 ± 8.6 years. Average total and free PSA levels were 19.77 ± 50.03 ng/mL and 2.46 ± 8.36 ng/mL respectively. Average free to total PSA ratio was 14.68 ± 11.24%. Prostate size was 58.58 ± 31.64CC on average. Age, total PSA, prostate volume and abnormal DRE were correlated with prostate cancer at biopsy, and the most powerful of all was abnormal DRE with odds ratio of 0.12. Neural networks were formed on a 3-layer perceptron and finally a network of 6 entry, 9 middle, and 2 output nodes was selected with the learning rate of 0.05. The Correct prediction rate for the model was 85.3%. Conclusions: It seems that our three-layer perceptron neural network model proves better results than the logistic regression model in predicting the presence of prostate cancer based on total and free PSA, DRE result, prostate volume and age.