CASIA OpenIR  > 中国科学院分子影像重点实验室
Artificial intelligence in gastric cancer: applications and challenges
Cao, Runnan1,2; Tang, Lei3; Fang, Mengjie1,2,6; Zhong, Lianzhen1,2; Wang, Siwen1,2; Gong, Lixin4,5; Li, Jiazheng3; Dong, Di1,2,8; Tian, Jie6,7
Source PublicationGASTROENTEROLOGY REPORT
ISSN2052-0034
2022-01-25
Volume10Pages:16
Corresponding AuthorDong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractGastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies. Recently, the developments of machine learning, especially deep-learning algorithms, have been facilitating computers to extract more information from data automatically. Researchers are exploring the far-reaching applications of artificial intelligence (AI) in various clinical practices, including GC. Herein, we aim to provide a broad framework to summarize current research on AI in GC. In the screening of GC, AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation. In the diagnosis of GC, AI can support tumor-node-metastasis (TNM) staging and subtype classification. For treatment decisions, AI can help with surgical margin determination and prognosis prediction. Meanwhile, current approaches are challenged by data scarcity and poor interpretability. To tackle these problems, more regulated data, unified processing procedures, and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.
Keywordgastric cancer artificial intelligence radiomics endoscopy computed tomography pathology
DOI10.1093/gastro/goac064
WOS KeywordHELICOBACTER-PYLORI INFECTION ; CONVOLUTIONAL NEURAL-NETWORKS ; ENDOSCOPIC ULTRASONOGRAPHY ; CT GASTROGRAPHY ; DIAGNOSIS ; IMAGES ; CLASSIFICATION ; RADIOMICS ; TUMORS ; RISK
Indexed BySCI
Language英语
Funding ProjectNational Natural ScienceFoundation of China ; National Key R&D Program of China[82022036] ; National Key R&D Program of China[91959130] ; National Key R&D Program of China[81971776] ; National Key R&D Program of China[62027901] ; National Key R&D Program of China[81930053] ; Beijing Natural Science Foundation[2017YFA0205200] ; Strategic Priority Research Program of Chinese Academy of Sciences[Z20J00105] ; Youth Innovation Promotion Association CAS[XDB38040200] ; [Y2021049]
Funding OrganizationNational Natural ScienceFoundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS Research AreaGastroenterology & Hepatology
WOS SubjectGastroenterology & Hepatology
WOS IDWOS:000892487000003
PublisherOXFORD UNIV PRESS
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50837
Collection中国科学院分子影像重点实验室
Corresponding AuthorDong, Di; Tian, Jie
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Inst Automat,State Key Lab Management & Control Co, Beijing, Peoples R China
3.Peking Univ Canc Hosp & Inst, Radiol Dept, Key Lab Carcinogenesis & Translat Res, Minist Educ Beijing, Beijing, Peoples R China
4.Northeastern Univ, Coll Med, Shenyang, Liaoning, Peoples R China
5.Northeastern Univ, Biol Informat Engn Sch, Shenyang, Liaoning, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
8.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Cao, Runnan,Tang, Lei,Fang, Mengjie,et al. Artificial intelligence in gastric cancer: applications and challenges[J]. GASTROENTEROLOGY REPORT,2022,10:16.
APA Cao, Runnan.,Tang, Lei.,Fang, Mengjie.,Zhong, Lianzhen.,Wang, Siwen.,...&Tian, Jie.(2022).Artificial intelligence in gastric cancer: applications and challenges.GASTROENTEROLOGY REPORT,10,16.
MLA Cao, Runnan,et al."Artificial intelligence in gastric cancer: applications and challenges".GASTROENTEROLOGY REPORT 10(2022):16.
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