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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neuro Oncol
Hauptverfasser: Su, Xiaorui, Chen, Ni, Sun, Huaiqiang, Liu, Yanhui, Yang, Xibiao, Wang, Weina, Zhang, Simin, Tan, Qiaoyue, Su, Jingkai, Gong, Qiyong, Yue, Qiang
Format: Artigo
Sprache:Inglês
Veröffentlicht: Oxford University Press 2020
Schlagworte:
Pediatric Neuro-Oncology
Online Zugang: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
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge