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.
Written by:
Benecchi L, Bocchi F, Martinotti M, Perucchini L, Quarta M, Russo F, Tonghini M, Del Boca C. Are you the author?
Divisione di Urologia, Ospedale di Cremona, Cremona - Italy.
Reference: Urologia. 2013 Feb 22;0(0):0.
doi: 10.5301/RU.2013.10765
PubMed Abstract
PMID: 23504865
Article in Italian.
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