Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography

SIMPLE SUMMARY: This study evaluates how advanced image analyses (radiomic features) and machine learning algorithms can help to distinguish subtypes of kidney tumors in computed tomography (CT) images, which is important for further patient treatment. For 201 patients, the image analyses showed a m...

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Detalles Bibliográficos
Autores principales: Uhlig, Johannes, Leha, Andreas, Delonge, Laura M., Haack, Anna-Maria, Shuch, Brian, Kim, Hyun S., Bremmer, Felix, Trojan, Lutz, Lotz, Joachim, Uhlig, Annemarie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603020/
https://www.ncbi.nlm.nih.gov/pubmed/33081400
http://dx.doi.org/10.3390/cancers12103010

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603020/
https://www.ncbi.nlm.nih.gov/pubmed/33081400
http://dx.doi.org/10.3390/cancers12103010

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