Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study

doi:10.1007/s10120-023-01451-9

	Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study
	Fang, Shuangshuang 1; Liu, Zhenyu2,3 ; Qiu, Qi2,3 ; Tang, Zhenchao 4; Yang, Yang 1; Kuang, Zhongsheng 5; Du, Xiaohua 6; Xiao, Shanshan 5; Liu, Yanyan5 ; Luo, Yuanbin 7; Gu, Liping 7; Tian, Li 7; Liang, Xiaoxia 8; Fan, Guiling 8; Zhang, Yu 8; Zhang, Ping 9; Zhou, Weixun 10; Liu, Xiuli 11; Tian, Jie 2,3,4,12; Wei, Wei 1
发表期刊	GASTRIC CANCER
ISSN	1436-3291
	2023-12-14
页码	12
通讯作者	Wei, Wei(weiwei3816@mail.cintcm.ac.cn)
摘要	ObjectivePatients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learning and OLGA/OLGIM for individual gastric cancer risk classification.MethodsIn this study, we prospectively enrolled 545 patients suspected of atrophic gastritis during endoscopy from 13 tertiary hospitals between December 22, 2017, to September 25, 2020, with a total of 2725 whole-slide images (WSIs). Patients were randomly divided into a training set (n = 349), an internal validation set (n = 87), and an external validation set (n = 109). Sixty patients from the external validation set were randomly selected and divided into two groups for an observer study, one with the assistance of algorithm results and the other without. We proposed a semi-supervised deep learning algorithm to diagnose and grade IM and atrophy, and we compared it with the assessments of 10 pathologists. The model's performance was evaluated based on the area under the curve (AUC), sensitivity, specificity, and weighted kappa value.ResultsThe algorithm, named GasMIL, was established and demonstrated encouraging performance in diagnosing IM (AUC 0.884, 95% CI 0.862-0.902) and atrophy (AUC 0.877, 95% CI 0.855-0.897) in the external test set. In the observer study, GasMIL achieved an 80% sensitivity, 85% specificity, a weighted kappa value of 0.61, and an AUC of 0.953, surpassing the performance of all ten pathologists in diagnosing atrophy. Among the 10 pathologists, GasMIL's AUC ranked second in OLGA (0.729, 95% CI 0.625-0.833) and fifth in OLGIM (0.792, 95% CI 0.688-0.896). With the assistance of GasMIL, pathologists demonstrated improved AUC (p = 0.013), sensitivity (p = 0.014), and weighted kappa (p = 0.016) in diagnosing IM, and improved specificity (p = 0.007) in diagnosing atrophy compared to pathologists working alone.ConclusionGasMIL shows the best overall performance in diagnosing IM and atrophy when compared to pathologists, significantly enhancing their diagnostic capabilities.
关键词	Atrophic gastritis Semi-supervised deep learning Diagnose The operative link for gastric intestinal metaplasia assessment The operative link for gastritis assessment
DOI	10.1007/s10120-023-01451-9
关键词[WOS]	PROSTATE-CANCER ; CLASSIFICATION ; AGREEMENT ; BIOPSIES ; SYSTEM
收录类别	SCI
语种	英语
资助项目	National Traditional Chinese Medicine Inheritance and Innovation Team Project
项目资助者	National Traditional Chinese Medicine Inheritance and Innovation Team Project
WOS研究方向	Oncology ; Gastroenterology & Hepatology
WOS类目	Oncology ; Gastroenterology & Hepatology
WOS记录号	WOS:001126296000001
出版者	SPRINGER
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55013
专题	中国科学院分子影像重点实验室
通讯作者	Wei, Wei
作者单位	1.China Acad Chinese Med Sci, Wangjing Hosp, Dept Med Secur Management, Beijing Key Lab Funct Gastrointestinal Disorders D, 6 Zhonghuan South Rd, Beijing 100102, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China 5.Guangdong Univ Tradit Chinese Med, Dept Pathol, Affiliated Hosp 1, Guangzhou 510405, Peoples R China 6.Guangdong Prov Hosp Tradit Chinese Med, Dept Pathol, Guangzhou 510120, Peoples R China 7.Gansu Prov Hosp Tradit Chinese Med, Dept Pathol, Lanzhou 730050, Peoples R China 8.Shanxi Prov Hosp Tradit Chinese Med, Dept Pathol, Taiyuan 030012, Peoples R China 9.Wangjing Hosp, China Acad Chinese Med Sci, Dept Pathol, Beijing 100102, Peoples R China 10.Peking Union Med Coll Hosp, Dept Pathol, Beijing 100730, Peoples R China 11.Washington Univ, Dept Pathol & Immunol, St Louis, MO 98195 USA 12.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Fang, Shuangshuang,Liu, Zhenyu,Qiu, Qi,et al. Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study[J]. GASTRIC CANCER,2023:12.
APA	Fang, Shuangshuang.,Liu, Zhenyu.,Qiu, Qi.,Tang, Zhenchao.,Yang, Yang.,...&Wei, Wei.(2023).Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study.GASTRIC CANCER,12.
MLA	Fang, Shuangshuang,et al."Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study".GASTRIC CANCER (2023):12.