Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

OBJECTIVE: To identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM). MATERIALS AND METHODS: This retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 m...

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Detalles Bibliográficos
Autores principales: Qian, Zenghui, Zhang, Lingling, Hu, Jie, Chen, Shuguang, Chen, Hongyan, Shen, Huicong, Zheng, Fei, Zang, Yuying, Chen, Xuzhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417735/
https://www.ncbi.nlm.nih.gov/pubmed/34490097
http://dx.doi.org/10.3389/fonc.2021.699789

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417735/
https://www.ncbi.nlm.nih.gov/pubmed/34490097
http://dx.doi.org/10.3389/fonc.2021.699789

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