Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning.

Journal: Gut
Published Date:

Abstract

OBJECTIVE: Tumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.

Authors

  • Jie-Yi Shi
    Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China.
  • Xiaodong Wang
    Cardiovascular Department, TEDA International Cardiovascular Hospital, Tianjin, China.
  • Guang-Yu Ding
    Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China.
  • Zhou Dong
    School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.
  • Jing Han
    Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education; School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China.
  • Zehui Guan
    School of Computer Science, Northwestern Polytechnical University, Xi'an, P. R. China.
  • Li-Jie Ma
    Department of General Surgery, Zhongshan Hospital (South), Public Health Clinical Centre, Fudan University, Shanghai, P. R. China.
  • Yuxuan Zheng
    School of Computer Science and Technology, Xidian University, Xi'an, P. R. China.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Guan-Zhen Yu
    Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, P. R. China.
  • Xiao-Ying Wang
  • Zhen-Bin Ding
    Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China.
  • Ai-Wu Ke
    Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Haoqing Yang
    3School of Computer Science and Technology, Xidian University, Xi'an, Shanxi China.
  • Liming Wang
    School of Information and Communication Engineering, North University of China, Taiyuan 030051, China. wlm@nuc.edu.cn.
  • Lirong Ai
    School of Computer Science, Northwestern Polytechnical University, Xi'an, P. R. China.
  • Ya Cao
    Cancer Research Institute, Xiangya School of Medicine, Central South University, Hunan, P. R. China.
  • Jian Zhou
    CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA.
  • Jia Fan
    Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Xiyang Liu
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi'an, 710071, China. xyliu@xidian.edu.cn.
  • Qiang Gao
    Faculty of Material Science and Chemistry, China University of Geosciences, Wuhan 430074, PR China.