Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

Journal: Liver international : official journal of the International Association for the Study of the Liver
Published Date:

Abstract

BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans.

Authors

  • Fang Wang
    Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China.
  • Gan Zhan
    College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
  • Qing-Qing Chen
    Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Hou-Yun Xu
    Department of Radiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China.
  • Dan Cao
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Yuan-Yuan Zhang
    School of Medicine, Shaoxing University, Shaoxing, China.
  • Yin-Hao Li
    College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
  • Chu-Jie Zhang
    Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
  • Yao Jin
    Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Wen-Bin Ji
    Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China.
  • Jian-Bing Ma
    Department of Radiology, The First Hospital of Jiaxing, The Affiliated Hospital of Jiaxing University, Jiaxing, China.
  • Yun-Jun Yang
    Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Zhi-Yi Peng
    Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Xiao Liang
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Beijing, 100193, People's Republic of China; College of Veterinary Medicine, Qingdao Agricultural University, Qingdao, 266109, People's Republic of China.
  • Li-Ping Deng
    Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Lan-Fen Lin
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
  • Yen-Wei Chen
  • Hong-Jie Hu
    Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.