Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.

Abstract

Authors

• Huan Yang
• Wenxi Wang
  Department of Magnetic Resonance Imaging, First Hospital of Qinhuangdao, Qinhuangdao, China.
• Zhiyong Cheng
  Department of Education, First Hospital of Qinhuangdao, Qinhuangdao, China.
• Tao Zheng
  Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China; Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China. Electronic address: zhengtao@ms.giec.ac.cn.
• Cheng Cheng
  School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.
• Mengyu Cheng
  Department of Magnetic Resonance Imaging, First Hospital of Qinhuangdao, Qinhuangdao, China.
• Zhanqiu Wang
  Department of Magnetic Resonance Imaging, First Hospital of Qinhuangdao, Qinhuangdao, China.

Keywords

External Resources