Artificial intelligence in gastric cancer:applications and challenges  

作　　者：Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian 

机构地区：[1]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,P.R.China [2]CAS Key Laboratory of Molecular Imaging,Beijing Key Laboratory of Molecular Imaging,the State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,P.R.China [3]Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing),Radiology Department,Peking University Cancer Hospital&Institute,Beijing,P.R.China [4]College of Medicine and Biological Information Engineering School,Northeastern University,Shenyang,Liaoning,P.R.China [5]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine,School of Engineering Medicine,Beihang University,Beijing,P.R.China [6]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education,School of Life Science and Technology,Xidian University,Xi’an,Shaanxi,P.R.China

出　　处：《Gastroenterology Report》2022年第1期227-242,共16页胃肠病学报道（英文）

基　　金：supported by the National Natural Science Foundation of China[grant numbers 82022036,91959130,81971776,62027901,81930053];National Key R&D Program of China[grant number 2017YFA0205200];the Beijing Natural Science Foundation[grant number Z20J00105];Strategic Priority Research Program of Chinese Academy of Sciences[grant number XDB38040200];the Youth Innovation Promotion Association CAS[grant number Y2021049].

摘　　要：Gastric 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.

关 键 词：gastric cancer artificial intelligence radiomics ENDOSCOPY computed tomography PATHOLOGY 

分 类 号：R730[医药卫生—肿瘤] TP18[医药卫生—临床医学]