Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain

BACKGROUND: Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas. METHODS: T...

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Detalhes bibliográficos
Publicado no:Neuro Oncol
Main Authors: Su, Xiaorui, Chen, Ni, Sun, Huaiqiang, Liu, Yanhui, Yang, Xibiao, Wang, Weina, Zhang, Simin, Tan, Qiaoyue, Su, Jingkai, Gong, Qiyong, Yue, Qiang
Formato: Artigo
Idioma:Inglês
Publicado em: Oxford University Press 2020
Assuntos:
Pediatric Neuro-Oncology
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7442326/
https://ncbi.nlm.nih.gov/pubmed/31563963
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/neuonc/noz184
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