DUGiDocsTexture analysis of the apparent diffusion coefficient focused on contrast-enhancing lesions in predicting survival for Bevacizumab-treated patients with recurrent Glioblastoma
dc.contributor.author
dc.date.accessioned
2023-06-02T12:11:13Z
dc.date.available
2023-06-02T12:11:13Z
dc.date.issued
2023-06-01
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dc.description.abstract
The authors analyzed the usefulness of Magnetic resonance texture analysis (MRTA) in predicting outcomes of patients with recurrent glioblastoma treated with bevacizumab. MRTA is a radiomics approach that aims to quantify macroscopic tissue heterogeneity by analyzing various parameters based on the distribution of pixel values. MRTA have been associated with glioma grade, molecular status, treatment response, and survival. In this study, they suggested that MRTA helps predict survival in patients with recurrent glioblastoma treated with bevacizumab
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
MDPI (Multidisciplinary Digital Publishing Institute)
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Reproducció digital del document publicat a: https://doi.org/10.3390/cancers15113026
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Cancers, 2023, vol. 15, núm. 11, p. 3026
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Articles publicats (D-IMAE)
dc.rights
Attribution 4.0 International (CC BY 4.0)
dc.rights.uri
dc.source
Lopez-Rueda, Antonio Puig Alcántara, Josep Thió i Fernández de Henestrosa, Santiag Moreno-Negrete, Javier Luis Zwanzger, Christian Pujol, Teresa Aldeco, Iban Pineda, Estela Valduvieco, Izaskun González, José Juan Oleaga, Laura 2023 Texture analysis of the apparent diffusion coefficient focused on contrast-enhancing lesions in predicting survival for Bevacizumab-treated patients with recurrent Glioblastoma Cancers 15 11 3026
dc.title
Texture analysis of the apparent diffusion coefficient focused on contrast-enhancing lesions in predicting survival for Bevacizumab-treated patients with recurrent Glioblastoma
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
036968
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
2072-6694