Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. MATERIALS AND METHODS: Two-hundred four patients with LGGs from our institut...

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Detalles Bibliográficos
Autores principales: Park, Yae Won, Choi, Yoon Seong, Ahn, Sung Soo, Chang, Jong Hee, Kim, Se Hoon, Lee, Seung-Koo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2019
Materias:
Neuroimaging and Head & Neck
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715562/
https://www.ncbi.nlm.nih.gov/pubmed/31464116
http://dx.doi.org/10.3348/kjr.2018.0814

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715562/
https://www.ncbi.nlm.nih.gov/pubmed/31464116
http://dx.doi.org/10.3348/kjr.2018.0814

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