Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.

Journal: Cancer medicine
Published Date: Mar 1, 2024

Abstract

BACKGROUND: Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis.

Authors

  • Xiuming Zhang
    Department of Pathology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Xiaotian Yu
    Department of Mathematics, University of Virginia, Charlottesville, USA.
  • Wenjie Liang
    Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Zhongliang Zhang
    School of Management, Hangzhou Dianzi University, Hangzhou, P. R. China.
  • Shengxuming Zhang
    Department of Computer Science and Technology, Zhejiang University, Hangzhou, P. R. China.
  • Linjie Xu
    Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
  • Han Zhang
    Johns Hopkins University, Baltimore, MD, USA.
  • Zunlei Feng
    Computer Science and Technology, Zhejiang University, 38# Zheda Road, Hangzhou, 310027, People's Republic of China.
  • Mingli Song
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Shi Feng
    Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, China.

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