Deep Learning-Based Classification of Hepatocellular Nodular Lesions on Whole-Slide Histopathologic Images.

Journal: Gastroenterology
Published Date: Jun 1, 2022

Abstract

BACKGROUND & AIMS: Hepatocellular nodular lesions (HNLs) constitute a heterogeneous group of disorders. Differential diagnosis among these lesions, especially high-grade dysplastic nodules (HGDNs) and well-differentiated hepatocellular carcinoma (WD-HCC), can be challenging, let alone biopsy specimens. We aimed to develop a deep learning system to solve these puzzles, improving the histopathologic diagnosis of HNLs (WD-HCC, HGDN, low-grade DN, focal nodular hyperplasia, hepatocellular adenoma), and background tissues (nodular cirrhosis, normal liver tissue).

Authors

  • Na Cheng
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China.
  • Yong Ren
    Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
  • Jing Zhou
  • Yiwang Zhang
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Deyu Wang
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Xiaofang Zhang
    School of Computer Science and Technology, Soochow University, Suzhou 215006, People's Republic of China.
  • Bing Chen
    Department of Critical Care Medicine, The Second Hospital of Tianjin Medical University, Tianjin, China.
  • Fang Liu
    The First Clinical Medical College of Gannan Medical University, Ganzhou 341000, Jiangxi Province, China.
  • Jin Lv
    Department of Pathology, FoShan First People's Hospital, Foshan, China.
  • Qinghua Cao
    Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Sijin Chen
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Hong Du
    Department of Pathology, GuangZhou First People's Hospital, Guangzhou, China.
  • Dayang Hui
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Zijin Weng
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Qiong Liang
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Bojin Su
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Luying Tang
    Department of Pathology, The Third Affiliated Hospital of Sun Yat-sen University Lingnan Hospital, Guangzhou, China.
  • Lanqing Han
    Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
  • Jianning Chen
    Department of Pathology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Chunkui Shao
    Department of Pathology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China.

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