Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 6...

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Detalles Bibliográficos
Autores principales: Liu, Zhen, Hong, Xuanke, Wang, Linglong, Ma, Zeyu, Guan, Fangzhan, Wang, Weiwei, Qiu, Yuning, Zhang, Xueping, Duan, Wenchao, Wang, Minkai, Sun, Chen, Zhao, Yuanshen, Duan, Jingxian, Sun, Qiuchang, Liu, Lin, Ding, Lei, Ji, Yuchen, Yan, Dongming, Liu, Xianzhi, Cheng, Jingliang, Zhang, Zhenyu, Li, Zhi-Cheng, Yan, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496393/
https://www.ncbi.nlm.nih.gov/pubmed/37697238
http://dx.doi.org/10.1186/s12885-023-11338-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496393/
https://www.ncbi.nlm.nih.gov/pubmed/37697238
http://dx.doi.org/10.1186/s12885-023-11338-8

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