The widespread use of prostate specific antigen (PSA) caused high rate of overdiagnosis. Overdiagnosis leads to unnecessary definitive treatments of prostate cancer (PCa) with detrimental side effects, such as erectile dysfunction and incontinence. The aim of this study was to evaluate the feasibility of an artificial neural network-based approach to develop a combinatorial model including prostate health index (PHI) and multiparametric magnetic resonance (mpMRI) to recognize clinically significant PCa at initial diagnosis.

A Combinatorial Neural Network Analysis Reveals a Synergistic Behaviour of Multiparametric Magnetic Resonance and Prostate Health Index in the Identification of Clinically Significant Prostate Cancer

Gentile, Francesco;
2022-01-01

Abstract

The widespread use of prostate specific antigen (PSA) caused high rate of overdiagnosis. Overdiagnosis leads to unnecessary definitive treatments of prostate cancer (PCa) with detrimental side effects, such as erectile dysfunction and incontinence. The aim of this study was to evaluate the feasibility of an artificial neural network-based approach to develop a combinatorial model including prostate health index (PHI) and multiparametric magnetic resonance (mpMRI) to recognize clinically significant PCa at initial diagnosis.
2022
Artificial neural network
MpMRI
Phi
Prostate cancer
Tumor markers
Humans
Magnetic Resonance Imaging
Magnetic Resonance Spectroscopy
Male
Neural Networks, Computer
Prostate
Retrospective Studies
Prostate-Specific Antigen
Prostatic Neoplasms
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12317/81186
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