Luigi Benecchi1. 1. Department of Urology, Fidenza Hospital, Parma, Italy. benecchi.luigi@libero.it
Abstract
OBJECTIVES: To develop a neuro-fuzzy system to predict the presence of prostate cancer. Neuro-fuzzy systems harness the power of two paradigms: fuzzy logic and artificial neural networks. We compared the predictive accuracy of our neuro-fuzzy system with that obtained by total prostate-specific antigen (tPSA) and percent free PSA (%fPSA). METHODS: The data from 1030 men (both outpatients and hospitalized patients) were used. All men had a tPSA level of less than 20 ng/mL. Of the 1030 men, 195 (18.9%) had prostate cancer. A neuro-fuzzy system was developed using the coactive neuro-fuzzy inference system model. RESULTS: The mean area under the receiver operating characteristic curve for the neuro-fuzzy system output was 0.799 +/- 0.029 (95% confidence interval 0.760 to 0.835), for tPSA, it was 0.724 +/- 0.032 (95% confidence interval 0.681 to 0.765), and for %fPSA, 0.766 +/- 0.024 (95% confidence interval 0.725 to 0.804). Furthermore, pairwise comparison of the area under the curves evidenced differences among %fPSA, tPSA, and neuro-fuzzy system's output (tPSA versus neuro-fuzzy system's output, P = 0.008; %fPSA versus neuro-fuzzy system's output, P = 0.032). The comparison at 95% sensitivity showed that the neuro-fuzzy system had the best specificity (31.9%). CONCLUSIONS: This study presented a neuro-fuzzy system based on both serum data (tPSA and %fPSA) and clinical data (age) to enhance the performance of tPSA to discriminate prostate cancer. The predictive accuracy of the neuro-fuzzy system was superior to that of tPSA and %fPSA.
OBJECTIVES: To develop a neuro-fuzzy system to predict the presence of prostate cancer. Neuro-fuzzy systems harness the power of two paradigms: fuzzy logic and artificial neural networks. We compared the predictive accuracy of our neuro-fuzzy system with that obtained by total prostate-specific antigen (tPSA) and percent free PSA (%fPSA). METHODS: The data from 1030 men (both outpatients and hospitalized patients) were used. All men had a tPSA level of less than 20 ng/mL. Of the 1030 men, 195 (18.9%) had prostate cancer. A neuro-fuzzy system was developed using the coactive neuro-fuzzy inference system model. RESULTS: The mean area under the receiver operating characteristic curve for the neuro-fuzzy system output was 0.799 +/- 0.029 (95% confidence interval 0.760 to 0.835), for tPSA, it was 0.724 +/- 0.032 (95% confidence interval 0.681 to 0.765), and for %fPSA, 0.766 +/- 0.024 (95% confidence interval 0.725 to 0.804). Furthermore, pairwise comparison of the area under the curves evidenced differences among %fPSA, tPSA, and neuro-fuzzy system's output (tPSA versus neuro-fuzzy system's output, P = 0.008; %fPSA versus neuro-fuzzy system's output, P = 0.032). The comparison at 95% sensitivity showed that the neuro-fuzzy system had the best specificity (31.9%). CONCLUSIONS: This study presented a neuro-fuzzy system based on both serum data (tPSA and %fPSA) and clinical data (age) to enhance the performance of tPSA to discriminate prostate cancer. The predictive accuracy of the neuro-fuzzy system was superior to that of tPSA and %fPSA.
Authors: Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino Journal: Future Oncol Date: 2009-12 Impact factor: 3.404
Authors: Georgina Cosma; Giovanni Acampora; David Brown; Robert C Rees; Masood Khan; A Graham Pockley Journal: PLoS One Date: 2016-06-03 Impact factor: 3.240