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MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic...

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Detalles Bibliográficos
Autores principales:	Dominguez, Ignacio, Rios-Ibacache, Odette, Caprile, Paola, Gonzalez, Jose, San Francisco, Ignacio F., Besa, Cecilia
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486695/ https://www.ncbi.nlm.nih.gov/pubmed/37685317 http://dx.doi.org/10.3390/diagnostics13172779

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