Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study.

Journal: European journal of radiology
Published Date: Nov 6, 2017

Abstract

PURPOSE: To assess the performance of Support Vector Machines (SVM) classification to stratify the Gleason Score (GS) of prostate cancer (PCa) in the central gland (CG) based on image features across multiparametric magnetic resonance imaging (mpMRI).

Authors

  • Jiance Li
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Zhiliang Weng
    Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Huazhi Xu
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Zhao Zhang
  • Haiwei Miao
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Zheng Liu
    ICSC World Laboratory, Geneva, Switzerland.
  • Xiaoqin Zhang
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Meihao Wang
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Xiao Xu
    State Key Laboratory for Molecular Virology and Genetic Engineering, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Qiong Ye
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China. Electronic address: 94301699@qq.com.

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