DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer.

Journal: Magnetic resonance imaging
PMID:

Abstract

OBJECTIVE: To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer.

Authors

  • Zhimin Ding
    Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China.
  • Chengmeng Zhang
    Department of Radiology, Huzhou Central Hospital, No. 1558 Third Ring North Road, Huzhou 313000, China.
  • Cong Xia
    Department of Radiology, Jiangsu Cancer Hospital, No. 42 BaiziTing Road, Xuanwu District, Nanjing 210000, China.
  • Qi Yao
    Department of Pharmacy The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Yi Wei
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province 610000, China.
  • Xia Zhang
    School of Computer Science, Engineering Northeastern University, No.195 Chuangxin Road Hunnan District, Shenyang 110169, China.
  • Nannan Zhao
    Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, No. 801 Zhihuai Road, Bengbu 233004, China.
  • Xiaoming Wang
    Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
  • Suhua Shi
    Department of Gynaecology and Obstetrics, The First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China. Electronic address: shisuhua1840@126.com.