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

Internet

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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