Validation of a Machine Learning Model Based on Multiparametric MR for the Prediction of Clinically Significant Prostate Cancer
The goal of this observational study is to validate a clinically significant predictive machine learning model based on the processing of images RMmp (Multiparametric Magnetic Resonance Imaging). To be validated the model should be evaluated on: * Specificity (SP): is the probability of a negative test result, conditioned on the individual truly being negative * Sensitivity (SN): is the probability of a positive test result, conditioned on the individual truly being positive
• Participants aged 18 at the time of examination
• Obtaining informed consent
• Presence of one or more lesions classified as PI-RADSv2.1 ≥ 1 at a prostate RMmp at the IRCCS Azienda Ospedaliero-Universitaria in Bologna
• Indication for TRUS biopsy by fusion technique integrated with systematic biopsy at the IRCCS Azienda Ospedaliero-Universitaria in Bologna