Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.

Journal: World journal of gastroenterology
Published Date: May 21, 2025

Abstract

BACKGROUND: Esophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can improve patient survival rates. The progression of esophageal cancer follows a sequence from esophagitis to non-dysplastic Barrett's esophagus, dysplastic Barrett's esophagus, and eventually esophageal adenocarcinoma (EAC). This study explored the application of deep learning technology in the precise diagnosis of pathological classification and staging of EAC to enhance diagnostic accuracy and efficiency.

Authors

  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Xiao-Lei Zhang
    Center for Intelligent Acoustics and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechnical University, China. Electronic address: xiaolei.zhang@nwpu.edu.cn.
  • Hong-Zhen Wang
    Department of Oncology, Dongying People's Hospital, Dongying 257091, Shandong Province, China.
  • Lin-Lin Wang
    Department of Pathology, Dongying People's Hospital, Dongying 257091, Shandong Province, China.
  • Jing-Li Wen
    Department of Oncology, Dongying People's Hospital, Dongying 257091, Shandong Province, China.
  • Xin Han
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Qian Liu
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.

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