Deep Learning–Based Stratification of Gastric Cancer Patients from Hematoxylin and Eosin–Stained Whole Slide Images by Predicting Molecular Features for Immunotherapy Response-Reference-Cited by-同舟云学术

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Deep Learning–Based Stratification of Gastric Cancer Patients from Hematoxylin and Eosin–Stained Whole Slide Images by Predicting Molecular Features for Immunotherapy Response

Published:2023-10 Issue:10 Volume:193 Page:1517-1527
ISSN:0002-9440
Container-title:The American Journal of Pathology
language:en
Short-container-title:The American Journal of Pathology

Author:

Wei Zheng,Zhao Xu,Chen Jing,Sun Qiuyan,Wang Zeyang,Wang Yanli,Ye Zhiyi,Yuan Yuan,Sun Liping,Jing Jingjing^ORCID

Funder

Department of Education of Liaoning Province

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Pathology and Forensic Medicine

Reference33 articles.

1. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries;Sung;CA Cancer J Clin,2021

2. Advances in clinical immunotherapy for gastric cancer;Li;Biochim Biophys Acta Rev Cancer,2021

3. Comprehensive molecular characterization of gastric adenocarcinoma;Nature,2014

4. Gastric cancer, version 2.2022, NCCN clinical practice guidelines in oncology;Ajani;J Natl Compr Cancer Netw,2022

5. The 2019 WHO classification of tumours of the digestive system;Nagtegaal;Histopathology,2020

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1. Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis;Cancers;2023-10-01

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