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

Journal: Neuro-oncology
Published Date: Mar 5, 2020

Abstract

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.

Authors

  • Xiaorui Su
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Ni Chen
  • Huaiqiang Sun
    Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China.
  • Yanhui Liu
    Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China.
  • Xibiao Yang
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Weina Wang
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Simin Zhang
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Qiaoyue Tan
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Jingkai Su
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Qiyong Gong
    Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Qiang Yue
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.

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