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Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Authors
  • Ma, Bowei1, 2
  • Guo, Yucheng1, 2
  • Hu, Weian2
  • Yuan, Fei3
  • Zhu, Zhenggang4
  • Yu, Yingyan4
  • Zou, Hao1, 2
  • 1 Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen , (China)
  • 2 Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen , (China)
  • 3 Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai , (China)
  • 4 Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai , (China)
Type
Published Article
Journal
Frontiers in Pharmacology
Publisher
Frontiers Media SA
Publication Date
Oct 02, 2020
Volume
11
Identifiers
DOI: 10.3389/fphar.2020.572372
PMID: 33132910
PMCID: PMC7562716
Source
PubMed Central
Keywords
License
Unknown

Abstract

Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.

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