Come Biopsiare Solo i Pazienti con Tumore Prostatico ad Alto Grado?. Issue 22 (April 2013)

• Record Type: Journal Article
• Authors: Benecchi, Luigi; Bocchi, Francesca; Martinotti, Mario; Perucchini, Laura; Quarta, Matteo; Russo, Fabrizio; Tonghini, Mario; Del Boca, Carlo
• Is Part Of: Urologia journal. Volume 80:Issue 22(2013)Supplement
• Page Start: 1
• Page End: 4
• Publication Date: 2013-04
• Subjects: Prostate cancer -- Prostate specific antigen -- Artificial neural network -- Tumore prostatico -- Antigene specifico prostatico -- Reti neurali; Urology -- Periodicals; 616.6005
• DOI: 10.5301/RU.2013.10765
• Languages: English
• ISSNs: 0391-5603

Abstract

Introduction: Fuzzy logic and Artificial Neural Networks (ANN) are complementary technologies that together generate neuro-fuzzy system. The aim of our study is to compare 2 models for predicting the presence of high-grade prostate cancer (Gleason score 7 or more). Methods: We evaluated data from 1000 men with PSA less than 50 ng/mL, who underwent prostate biopsy. A prostate cancer was found in 313 (31%), and in 172 (17.2%) we detected high-grade prostate cancer. With those data, we developed 2 Co-Active Neuro-Fuzzy Inference Systems to predict the presence of high-grade prostate cancer. The first model had four input neurons (PSA, free PSA percentage [%freePSA], PSA density, and age) and the second model had three input neurons (PSA, %freePSA, and age). Results: The model with four input neurons (PSA, %freePSA, PSA density, and age) showed better performances than the one with three input neurons (PSA, %freePSA, and age). In fact the average testing error was 0.42 for the model with four input neurons and 0.44 for the other model. Conclusions: The addition of PSA density to the model has allowed to obtain better results for the diagnosis of high grade prostate cancer.