A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)

Background: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. Purpose: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic...

Descripción completa

Detalles Bibliográficos
Autores principales: Garnier, Cassandre, Ferrer, Loïc, Vargas, Jennifer, Gallinato, Olivier, Jambon, Eva, Le Bras, Yann, Bernhard, Jean-Christophe, Colin, Thierry, Grenier, Nicolas, Marcelin, Clément
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417436/
https://www.ncbi.nlm.nih.gov/pubmed/37568911
http://dx.doi.org/10.3390/diagnostics13152548

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417436/
https://www.ncbi.nlm.nih.gov/pubmed/37568911
http://dx.doi.org/10.3390/diagnostics13152548

Ejemplares similares