A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma.

Journal: European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Published Date: Sep 6, 2022

Abstract

BACKGROUND: Accurate preoperative identification of the microvascular invasion (MVI) can relieve the pressure from personalized treatment adaptation and improve the poor prognosis for hepatocellular carcinoma (HCC). This study aimed to develop and validate a novel multimodal deep learning (DL) model for predicting MVI based on multi-parameter magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CT).

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.
  • Qingqing Chen
  • Yinan Chen
    12 Sigma Technologies, NO. 420 Fenglin Road, Xuhui District, Shanghai, China.
  • Yajing Zhu
    12 Sigma Technologies, NO. 420 Fenglin Road, Xuhui District, Shanghai, China.
  • Yuanyuan Zhang
    National Clinical Research Center for Kidney Disease, State Key Laboratory for Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, 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.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, 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.
  • Yunjun Yang
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China. yyjunjim@163.com.
  • Lanfen Lin
    State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310027, China.
  • Hongjie Hu

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