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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Detalles Bibliográficos
Autores principales: Prata, Francesco, Anceschi, Umberto, Cordelli, Ermanno, Faiella, Eliodoro, Civitella, Angelo, Tuzzolo, Piergiorgio, Iannuzzi, Andrea, Ragusa, Alberto, Esperto, Francesco, Prata, Salvatore Mario, Sicilia, Rosa, Muto, Giovanni, Grasso, Rosario Francesco, Scarpa, Roberto Mario, Soda, Paolo, Simone, Giuseppe, Papalia, Rocco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955797/
https://www.ncbi.nlm.nih.gov/pubmed/36826118
http://dx.doi.org/10.3390/curroncol30020157

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955797/
https://www.ncbi.nlm.nih.gov/pubmed/36826118
http://dx.doi.org/10.3390/curroncol30020157

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