Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram.

Journal: Clinical radiology
Published Date: Jul 8, 2022

Abstract

AIM: To investigate the performance of a generative adversarial network (GAN) model for staging liver fibrosis and its radiomics-based nomogram for predicting cirrhosis.

Authors

  • Y-Y Duan
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China.
  • J Qin
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China.
  • W-Q Qiu
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China.
  • S-Y Li
    Department of Ultrasound, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, No. 20 Yuhuangdingdong Road, Zhifu District, Yantai 264099, Shandong Province, China.
  • C Li
    Department of Animal Sciences, University of Florida, Gainesville 32611.
  • A-S Liu
    Department of Ultrasound, The First Affiliated Hospital of Anhui University of Chinese Medicine, No. 117 Meishan Road, Shushan District, Hefei 230022, Anhui Province, China.
  • X Chen
    Division of Infectious Diseases,The People's Hospital of Meizhou,Meizhou,China.
  • C-X Zhang
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China. Electronic address: zcxay@163.com.

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