Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.

Journal: Academic radiology
PMID:

Abstract

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).

Authors

  • Guihan Lin
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China.
  • Weiyue Chen
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China.
  • Yongjun Chen
    Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Changsheng Shi
    Department of Interventional Vascular Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
  • Qianqian Cao
    Department of Pathology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
  • Yang Jing
    Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China.
  • Weiming Hu
  • Ting Zhao
    Department of Neurology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Henan Province, China.
  • Pengjun Chen
    Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
  • Zhihan Yan
    Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, China.
  • Minjiang Chen
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China.
  • Chenying Lu
    Departments of Medicine and Radiology, State University of New York, Upstate Medical University Hospital, Syracuse, USA.
  • Shuiwei Xia
    Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
  • Jiansong Ji
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China.