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中华胃肠内镜电子杂志 ›› 2019, Vol. 06 ›› Issue (02) : 82 -85. doi: 10.3877/cma.j.issn.2095-7157.2019.02.008

所属专题: 文献

综述

人工智能在消化系肿瘤的研究进展
田永刚1, 曹贞子2, 白飞虎2,(), 韩丽伟3   
  1. 1. 750004 银川,宁夏医科大学
    2. 750021 银川,宁夏回族自治区人民医院消化内科
    3. 750021 银川,宁夏回族自治区人民医院手术室
  • 收稿日期:2019-04-18 出版日期:2019-05-15
  • 通信作者: 白飞虎
  • 基金资助:
    国家自然科学基金项目(81760440,81860426); 中央引导地方科技发展专项(YDZX20176400004650); 宁夏消化疾病临床医学研究中心(2018CXPT0075)

Research progress of artificial intelligence in digestive system tumors

Yonggang Tian1, Zhenzi Cao2, Feihu Bai2,(), Liwei Han3   

  1. 1. Ningxia Medical College, Ningxia Yinchuan 750004, China
    2. Department of Gastroenterology, Ningxia Hui Autonomous Region People′s Hospital, Yinchuan 750021, China
    3. Operating room, Ningxia Hui Autonomous Region People′s Hospital, Yinchuan 750021, China
  • Received:2019-04-18 Published:2019-05-15
  • Corresponding author: Feihu Bai
  • About author:
    Correspondence author: Bai Feihu, Email:
引用本文:

田永刚, 曹贞子, 白飞虎, 韩丽伟. 人工智能在消化系肿瘤的研究进展[J]. 中华胃肠内镜电子杂志, 2019, 06(02): 82-85.

Yonggang Tian, Zhenzi Cao, Feihu Bai, Liwei Han. Research progress of artificial intelligence in digestive system tumors[J]. Chinese Journal of Gastrointestinal Endoscopy(Electronic Edition), 2019, 06(02): 82-85.

伴随着人工智能技术在医疗领域的迅猛发展,强大的计算和深度学习能力已引起了全球医疗领域人士的共同关注。尤其是近年来,人工智能在消化系统肿瘤方面的应用取得了显著的发展,为临床医师诊治消化系肿瘤提供了一种全新的"心理-社会-生物医学-人工智能"的诊疗新模式,也为患有消化系肿瘤的患者带来新的精准诊治方案。本文对人工智能在消化系肿瘤的研究进展进行如下综述。

With the rapid development of artificial intelligence technology in the medical field, its powerful computing and deep learning capabilities have attracted the attention of people in the global medical field. Especially in recent years, the application of artificial intelligence in the diagnosis of digestive system tumors has also made significant progress, providing a new kind of "psycho-social-biomedical-artificial intelligence" diagnosis and treatment for clinicians to treat digestive system tumors.The model also brings new precision diagnosis and treatment to patients with digestive system tumors.In view of this, this paper reviews the research progress of artificial intelligence in digestive system tumors.

[1]
MusibM,WangF,TarselliMA,et al.Artificial intelligence in research[J].Science,2017,357(6346):28-30.
[2]
TingDSW,PasqualeLR,PengL,et al.Artificial intelligence and deep learning in ophthalmology[J].Br J Ophthalmol,2019,103(2):167-175.
[3]
LeCunY,BengioY,HintonG.Deep learning[J].Nature,2015,521(7553):436-444.
[4]
PennathurA,GibsonMK,JobeBA.Oesophageal carcinoma[J].Lancet,2013,381(9864):400-412.
[5]
ChenW,ZhengR,BaadePD,et al.Cancer statistics in China, 2015[J]. CA Cancer J Clin,2016,66(2):115-132.
[6]
腾讯发布一个AI神器有望攻克食管癌早筛难题[J].信息与电脑:理论版,2017,(15):5.
[7]
GrahamDG,KhanS,SehgalV,et al.Combined Analysis of Salivary RNA Expression and Demographic, Symptom and Risk Factor Data Can Accurately Predict Those at Risk of Developing or With Esophageal Cancer[J].Gastroenterology,2016,150(4):S69-S70.
[8]
JohansonJF,FrakesJ,EisenD.Endo CDx Collaborative Group.Computer-assisted analysis of abrasive transepithelial brush biopsies increases the effectiveness of esophageal screening:a multicenter prospective clinical trial by the Endo CDx Collaborative Group[J].Dig Dis Sci,2011,56(3):767-772.
[9]
ChanDK,ZakkoL,VisrodiaKH,et al.Breath Testing for Barrett’s Esophagus Using Exhaled Volatile Organic Compound Profiling With an Electronic Nose Device[J]. Gastroenterology,2017,152(1):24-26.
[10]
ChanDK,LutzkeLS,ClemensMA,et al.Detection of Barrett’s Esophagus by Non-invasive Breath Screening of Exhaled Volatile Organic Compounds Using an ElectronicNose Device[J].Gastroenterology,2016,150(4):S67.
[11]
LagergrenJ,SmythE,CunninghamD, et al. Oesophageal cancer[J]. Lancet, 2017, 390 (10110) :2383-2396..
[12]
HsuPK,HuangCS,WuYC,et al.Open versus thoracoscopic esophagectomy in patients with esophageal squamous cell carcinoma[J].World J Surg,2014,38(2):402-409.
[13]
QureshiYA,DawasKI,MughalM,et al.Minimally invasive and robotic esophagectomy: Evolution and evidence[J].J Surg Oncol,2016,114(6):731-735.
[14]
BizekisC,KentMS,LuketichJD,et al.Initial experience with minimally invasive Ivor Lewis esophagectomy[J].Ann Thorac Surg,2006,82(2):402-406.
[15]
SarkariaIS,RizkNP,FinleyDJ,et al.Combined thoracoscopic and laparoscopic robotic-assisted minimally invasive esophagectomy using a four-arm platform:experience, technique and cautions during early procedure development[J].Eur J Cardiothorac Surg, 2013,43(5):e107-e115.
[16]
HodariA,ParkKU,LaceB,et al.Robot-assisted minimally invasive Ivor Lewis esophagectomy with real-time perfusion assessment[J].Ann Thorac Surg,2015,100(3):947-952.
[17]
CerfolioRJ,BryantAS,HawnMT.Technical aspects and early results of robotic esophagectomy with chest anastomosis[J].J Thorac Cardiovasc Surg,2013,145(1):90-96.
[18]
ParkS,HwangY,LeeHJ,et al.Comparison of robot-assisted esophagectomy and thoracoscopic esophagectomy in esophageal squamous cell carcinoma[J].J Thorac Dis,2016,8(10):2853-2861.
[19]
ChaoYK,HsiehMJ,LiuYH,et al.Lymph node evaluation in robot-assisted versus video-assisted thoracoscopic esophagectomy for esophageal squamous cell carcinoma: a propensity-matched analysis[J].World J Surg,2018,42(2):590-598.
[20]
BrayF,FerlayJ,SoerjomataramI,et al.Global cancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36cancers in 185 countries[J].CA Cancer J Clin,2018,68(6):394-424.
[21]
ArantesV,UedoN,PedrosaMS,et al.Clinical relevance of aberrant polypoid nodule scar after endoscopic submucosal dissection[J].World J Gastrointest Endosc,2016,8(17):628-634.
[22]
RoTH,MathewMA,MisraS.Value of screening endoscopy in evaluation of esophageal, gastric and colon cancers[J].World J Gastroenterol,2015,21(33):9693-9706.
[23]
CheonJH.Advances in the endoscopic assessment of inflammatory bowel diseases:cooperation between endoscopic and pathologic evaluations[J].J Pathol Transl Med,2015,49(3):209-217.
[24]
TriadafilopoulosG,AkiyamaJ.Emerging endoscopic techniques for the identification of esophageal disease[J].Expert Rev Gastroenterol Hepatol,2016,10(5):605-613.
[25]
IshikawaH,IwamuroM,OkadaH,et al.Recurrence after radiotherapy for gastric mucos A-associated lymphoid tissue(MALT) lymphoma with trisomy 18[J].Intern Med,2015,54(8):911-916.
[26]
SharmaH,ZerbeN,KlempertI,et al.Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology [J].Comput Med Imaging Graph,2017,61(1):2-13.
[27]
YoshidaH,ShimazuT,KiyunaT,et al.Automated histological classification of whole-slide images of gastric biopsy specimens[J].Gastric Cancer,2018,21(2):249-257.
[28]
左婷婷,郑荣寿,曾红梅,等.中国肝癌发病状况与趋势分析[J].中华肿瘤杂志,2015,37(9):691-696.
[29]
ZengH,ChenW,ZhengR,et al.Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries[J].Lancet Glob Health,2018,6(5):e555-e567.
[30]
James SL,HendersonEE,ShatzelJJ,et al.Machine Learning Classifiers:A Novel Approach to Predicting Bleeding Risk in Hospitalized Cirrhotic Patients[J].Gastroenterology,2015,148(4):S1079.
[31]
ReddyR,ImlerTD.Artificial neural networks are highly predictive for hepatocellular carcinoma in patients with cirrhosis[J].Gastroenterology,2017,152(5):S1193.
[32]
StrebaCT,VereCC,SandulescuLD,et al.Focal Liver Lesions Classification by Artificial Neural Networks and Support Vector Machines Employing Dynamic Imaging Data[J].Gastroenterology,2014,146(5):S933.
[33]
HashimotoDA,RosmanG,RusD,et al.MEIRELESO R.Artificial intelligence in surgery:promises and perils[J].Ann Surg,2018,268(1):70-76.
[34]
van ErningFN,MackayTM,van der GeestLGM,et al.Association of the location of pancreatic ductal adenocarcinoma (head,body,tail) with tumor stage,treatment,and survival:a population-based analysis[J].Acta Oncol,2018,57(12):1655-1662.
[35]
SahinIH,EliasH,ChouJF,et al.Pancreatic adenocarcinoma:insights into patterns of recurrence and disease behavior[J].BMC Cancer,2018,18(1):769.
[36]
HeC,ZhangY,CaiZ,et al.Overall survival and cancer-specific survival in patients with surgically resected pancreatic head adenocarcinoma:A competing risk nomogram analysis[J].J Cancer,2018,9(17):3156-3167.
[37]
MahmudM,PiwoniA,FilipczakN,et al.Long-circulating curcumin-loaded liposome formula (tions with high incorporation efficiency,stability and anticancer activity towards pancreatic adenocarcinoma cell lines in vitro[J].PLoS One,2016,11(12):e0167787.
[38]
SftoiuA,VilmannP,GorunescuF,et al.European EUS Elastography Multicentric Study Group.Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses[J].Clin Gastroenterol Hepatol,2012,10(1):84-90.
[39]
BrennerH,Chang-ClaudeJ,JansenL,et al.Reduced risk of colorectal cancer up to 10 years after screening,surveillance,or diagnostic colonoscopy[J].Gastroenterology,2014,146(3):709-717.
[40]
ZauberAG,WinawerSJ,O′brienMJ,et al.Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths[J].New Engl J Med,2012,366(8):687-696.
[41]
WinawerSJ,ZauberAG,HoMN,et al.Prevention of colorectal cancer by colonoscopic polypectomy[J].New Engl J Med,1993,329(27):1977-1981.
[42]
MahmudN,CohenJ,TsouridesK,et al.Computer vision and augmented reality in gastrointestinal endoscopy[J].Gastroenterol Report,2015,3(3):179-184.
[43]
AhnSB,HanDS,BaeJH,et al.The miss rate for colorectal adenoma determined by quality-adjusted,back-to-back colonoscopies[J].Gut Liver,2012,6(1):64.
[44]
WangP,BerzinTM,BrownJRG,et al.Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates:a prospective randomised controlled study[J].Gut, 2019,Feb 27. pii:gutjnl-2018-317500. doi:10.1136/gutjnl-2018-317500.[Epub ahead of print]
[45]
WangP,XiaoX,BrownJRG,et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy[J].Nature Biomedical Engineering,2018,2(10):741.
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