Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features

Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features

Background: The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer. Methods: From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer a...

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Bibliographic Details
Main Authors: Francesco Prata, Umberto Anceschi, Ermanno Cordelli, Eliodoro Faiella, Angelo Civitella, Piergiorgio Tuzzolo, Andrea Iannuzzi, Alberto Ragusa, Francesco Esperto, Salvatore Mario Prata, Rosa Sicilia, Giovanni Muto, Rosario Francesco Grasso, Roberto Mario Scarpa, Paolo Soda, Giuseppe Simone, Rocco Papalia
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Current Oncology
Subjects:
clinically significant
machine-learning
prostate biopsy
prostate cancer
radiomic
Online Access:https://www.mdpi.com/1718-7729/30/2/157

Internet

https://www.mdpi.com/1718-7729/30/2/157

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