LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 30, 2022

Abstract

BACKGROUND AND OBJECTIVE: Liver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an important indicator for disease analysis and prognosis of patients. Child-Pugh score is the most widely used liver reserve function evaluation and scoring system. However, this method also has many shortcomings such as poor accuracy and subjective factors. To achieve comprehensive evaluation of liver reserve function, we developed a deep learning model to fuse bimodal features of Child-Pugh score and computed tomography (CT) image.

Authors

  • Zhiwei Huang
    Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576, Singapore.
  • Guo Zhang
    CHESS-COVID-19 Group, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
  • Jiong Liu
    Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
  • Mengping Huang
    Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
  • Lisha Zhong
    School of Medical Information and Engineering, Southwest Medical University, Luzhou, China. Electronic address: zhonglisha@swmu.edu.cn.
  • Jian Shu
    Key Laboratory of Analysis and Detection for Food Safety (MOE and Fujian Province), Collaborative Innovation Center of Detection Technology for Haixi Food Safety and Products (Fujian Province), State Key Laboratory of Photocatalysis on Energy and Environment, Department of Chemistry, Fuzhou University , Fujian Province, China , 350002.

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