人工智能技术在胃癌诊断与治疗中的实践与展望

国际肿瘤学杂志››2023,Vol. 50››Issue (11): 677-682.doi:10.3760/cma.j.cn371439-20230510-00128

人工智能技术在胃癌诊断与治疗中的实践与展望

李佳宜¹^,²^,³, 王跃¹^,²^,³, 尚兰兰¹^,², 徐兴²^,³^,⁴, 赵岩¹^,²^,³^,⁴()

¹中国医科大学研究生院，沈阳 110122
²辽宁省肿瘤医院（辽宁省肿瘤研究所）胃外科一病区，沈阳 110042
³辽宁省医工交叉重点实验室，沈阳 110042
⁴大连理工大学附属肿瘤医院胃外科一病区，沈阳 110042

收稿日期:2023-05-10修回日期:2023-07-02出版日期:2023-11-08发布日期:2024-01-11
通讯作者:赵岩 E-mail:dr.zhaoyan@126.com
基金资助:
辽宁省省级科技计划项目（重大项目）——“揭榜挂帅”科技攻关项目(2022JH1/10400004);2021年辽宁省民生科技计划(2021JH2/10300061)

Practice and prospect of artificial intelligence in diagnosis and treatment of gastric cancer

Li Jiayi¹^,²^,³, Wang Yue¹^,²^,³, Shang Lanlan¹^,², Xu Xing²^,³^,⁴, Zhao Yan¹^,²^,³^,⁴()

¹Graduate School，China Medical University，Shenyang 110122，China
²First Department of Gastric Surgery，Liaoning Cancer Hospital （Liaoning Cancer Institute），Shenyang 110042，China
³Liaoning Provincial Key Laboratory of Medical Engineering，Shenyang 110042，China
⁴First Department of Gastric Surgery，Cancer Hospital of Dalian University of Technology，Shenyang 110042，China

Received:2023-05-10Revised:2023-07-02Online:2023-11-08Published:2024-01-11
Contact:Zhao Yan E-mail:dr.zhaoyan@126.com
Supported by:
Liaoning Province Provincial Science and Technology Plan Project （Major Project）—"Uncovering the List" Science and Technology Research Project(2022JH1/10400004);Liaoning Province Livelihood Science and Technology Plan in 2021(2021JH2/10300061)

摘要/Abstract

摘要：

近年来，人工智能（AI）与肿瘤学迅速融合发展并逐渐走向临床应用。在我国，胃癌的发病率和死亡率在各种肿瘤中位居前列，卷积神经网络、窄带成像放大内窥镜、全视野数字切片、生存循环网络等AI技术的开发，具有应用于胃癌早期筛查、辅助诊断、病理学诊断、预后分析、制定个性化诊疗等方面的潜能。但是AI医疗仍然存在数据样本量不足和整合困难等多种问题。合理设计和应用AI技术有望为胃癌患者提供更为科学准确的诊疗方案，提高胃癌患者的总生存率和生命质量，在医疗领域实现更多突破。

关键词:人工智能,深度学习,胃肿瘤,治疗

Abstract:

In recent years，artificial intelligence （AI） and oncology are rapidly integrated and developed，gradually moving towards clinical application. In China，the incidence and mortality of gastric cancer rank first among various tumors. The development of AI technologies such as convolutional neural network，narrow-band imaging magnifying endoscope，full-field digital section and survival circulation network has the potential to be applied to early screening，auxiliary diagnosis，pathological diagnosis，prognosis analysis，as well as individualized diagnosis and treatment process of gastric cancer. However，AI medical treatment still has many problems such as insufficient data sample size and difficulty in integration. Reasonable design and application of AI technology is expected to provide more scientific and accurate diagnosis and treatment for patients with gastric cancer，improve the overall survival rate and quality of life of patients with gastric cancer，and achieve more breakthroughs in the medical field.

Key words:Artificial intelligence,Deep learning,Stomach neoplasms,Therapy

李佳宜, 王跃, 尚兰兰, 徐兴, 赵岩. 人工智能技术在胃癌诊断与治疗中的实践与展望[J]. 国际肿瘤学杂志, 2023, 50(11): 677-682.

Li Jiayi, Wang Yue, Shang Lanlan, Xu Xing, Zhao Yan. Practice and prospect of artificial intelligence in diagnosis and treatment of gastric cancer[J]. Journal of International Oncology, 2023, 50(11): 677-682.

参考文献35

[1]	中国疾病预防控制中心慢性非传染性疾病预防控制中心, 国家卫生和计划生育委员会统计信息中心.中国死因监测数据集-2016[M]. 北京: 中国科学技术出版社,2017: 26.
[2]	张世豪, 冼丽英, 高敏, 等. 基于深度学习的人工智能在病理诊断的应用进展与展望[J].中国医学创新,2018,15(25): 130-133. DOI:10.3969/j.issn.1674-4985.2018.25.035.
[3]	Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch[J].J Mach Learn Res,2011,12: 2493-2537. DOI:10.5555/1953048.2078186.
[4]	Simon G, DiNardo CD, Takahashi K, et al. Applying artificial intelligence to address the knowledge gaps in cancer care[J].Oncologist,2019,24(6): 772-782. DOI:10.1634/theoncologist.2018-0257. pmid:30446581
[5]	Zheng RS, Zhang SW, Zeng HM, et al. Cancer incidence and mortality in China, 2016[J].Journal of the National Cancer Center,2022,2(1): 1-68. DOI:10.1016/j.jncc.2022.02.002.
[6]	Szeliski R.Computer vision: algorithms and applications (texts in computer science)[M]. London: Springer,2011. DOI:10.1007/978-1-84882-935-0.
[7]	Togashi K. Applications of artificial intelligence to endoscopy practice: the view from Japan Digestive Disease Week 2018[J].Dig Endosc,2019,31(3): 270-272. DOI:10.1111/den.13354. pmid:30681203
[8]	Watanabe Y, Oikawa R, Agawa S, et al. Combination of artificial intelligence-based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis[J].J Clin Lab Anal,2022,36(1): e24122. DOI:10.1002/jcla.24122.
[9]	Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images[J].Gastric Cancer,2018,21(4): 653-660. DOI:10.1007/s10120-018-0793-2. pmid:29335825
[10]	Sakai Y, Takemoto S, Hori K, et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network[J].Annu Int Conf IEEE Eng Med Biol Soc,2018,2018: 4138-4141. DOI:10.1109/EMBC.2018.8513274. pmid:30441266
[11]	Yu T, Wang X, Zhao Z, et al. Prediction of T stage in gastric carcinoma by enhanced CT and oral contrast-enhanced ultrasonography[J].World J Surg Oncol,2015,13: 184. DOI:10.1186/s12957-015-0577-7. pmid:25986541
[12]	Choi J, Kim SG, Im JP, et al. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer[J].Endoscopy,2010,42(9): 705-713. DOI:10.1055/s-0030-1255617. pmid:20652857
[13]	Liu DY, Gan T, Rao NN, et al. Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process[J].Med Image Anal,2016,32: 281-294. DOI:10.1016/j.media.2016.04.007.
[14]	Li L, Chen Y, Shen Z, et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging[J].Gastric Cancer,2020,23(1): 126-132. DOI:10.1007/s10120-019-00992-2. pmid:31332619
[15]	Luo H, Xu G, Li C, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study[J].Lancet Oncol,2019,20(12): 1645-1654. DOI:10.1016/S1470-2045(19)30637-0. pmid:31591062
[16]	Huang Z, Liu D, Chen X, et al. Retrospective imaging studies of gastric cancer: study protocol clinical trial (SPIRIT Compliant)[J].Medicine (Baltimore),2020,99(8): e19157. DOI:10.1097/MD.0000000000019157.
[17]	Li C, Shi C, Zhang H, et al. Multiple instance learning for computer aided detection and diagnosis of gastric cancer with dual-energy CT imaging[J].J Biomed Inform,2015,57: 358-368. DOI:10.1016/j.jbi.2015.08.017. pmid:26319541
[18]	Mukhopadhyay S, Feldman MD, Abels E, et al. Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (pivotal study)[J].Am J Surg Pathol,2018,42(1): 39-52. DOI:10.1097/PAS.0000000000000948. pmid:28961557
[19]	Li Y, Li X, Xie X, et al. Deep learning based gastric cancer identification[C]//2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington: IEEE,2018: 182-185. DOI:10.1109/ISBI.2018.8363550.
[20]	Iizuka O, Kanavati F, Kato K, et al. Deep learning models for histopathological classification of gastric and colonic epithelial tumours[J].Sci Rep,2020,10(1): 1504. DOI:10.1038/s41598-020-58467-9. pmid:32001752
[21]	Sato Y, Sese J, Matsuyama T, et al. Preliminary study for develo-ping a navigation system for gastric cancer surgery using artificial intelligence[J].Surg Today,2022,52(12): 1753-1758. DOI:10.1007/s00595-022-02508-5.
[22]	金驰, 王彤. 达芬奇机器人在胃癌手术中的应用进展[J].临床外科杂志,2020,28(10): 991-994. DOI:10.3969/j.issn.1005-6483.2020.10.028.
[23]	王晓鹏, 郭进, 李渊, 等. 达芬奇机器人联合淋巴示踪在进展期远端胃癌根治术中的应用[J].中国微创外科杂志,2018,18(3): 225-229. DOI:10.3969/j.issn.1009-6604.2018.03.009.
[24]	Adballah M, Espinel Y, Calvet L, et al. Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours[J].Surg Endosc,2022,36(1): 833-843. DOI:10.1007/s00464-021-08798-z.
[25]	Luo H, Yin D, Zhang S, et al. Augmented reality navigation for liver resection with a stereoscopic laparoscope[J].Comput Methods Programs Biomed,2020,187: 105099. DOI:10.1016/j.cmpb.2019.105099.
[26]	Lee J, An JY, Choi MG, et al. Deep learning-based survival analysis identified associations between molecular subtype and optimal adjuvant treatment of patients with gastric cancer[J].JCO Clin Cancer Inform,2018,2: 1-14. DOI:10.1200/CCI.17.00065. pmid:30652558
[27]	Jiang Y, Xie J, Han Z, et al. Immunomarker support vector machine classifier for prediction of gastric cancer survival and adjuvant chemotherapeutic benefit[J].Clin Cancer Res,2018,24(22): 5574-5584. DOI:10.1158/1078-0432.CCR-18-0848. pmid:30042208
[28]	Lu F, Chen Z, Yuan X, et al. MMHG:multi-modal hypergraph learning for overall survival after D 2 gastrectomy for gastric cancer[C]//2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech).Orlando: IEEE,2017: 164-169. DOI:10.1109/DASC-PICom-DataCom-CyberSciTec.2017.40.
[29]	Korhani Kangi A, Bahrampour A. Predicting the survival of gastric cancer patients using artificial and bayesian neural networks[J].Asian Pac J Cancer Prev,2018,19(2): 487-490. DOI:10.22034/APJCP.2018.19.2.487.
[30]	Jiang Y, Zhang Z, Yuan Q, et al. Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study[J].Lancet Digit Health,2022,4(5): e340-e350. DOI:10.1016/S2589-7500(22)00040-1.
[31]	Hensler K, Waschulzik T, Mönig SP, et al. Quality-assured Efficient Engineering of Feedforward Neural Networks (QUEEN)-pretherapeutic estimation of lymph node status in patients with gastric carcinoma[J].Methods Inf Med,2005,44(5): 647-654. pmid:16400373
[32]	Jagric T, Potrc S, Jagric T. Prediction of liver metastases after gastric cancer resection with the use of learning vector quantization neural networks[J].Dig Dis Sci,2010,55(11): 3252-3261. DOI:10.1007/s10620-010-1155-z.
[33]	Su F, Sun Y, Hu Y, et al. Development and validation of a deep learning system for ascites cytopathology interpretation[J].Gastric Cancer,2020,23(6): 1041-1050. DOI:10.1007/s10120-020-01093-1.
[34]	Nakahira H, Ishihara R, Aoyama K, et al. Stratification of gastric cancer risk using a deep neural network[J].JGH Open,2019,4(3): 466-471. DOI:10.1002/jgh3.12281.
[35]	Zhu Y, Wang QC, Xu MD, et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy[J].Gastrointest Endosc,2019,89(4): 806-815.e1. DOI:10.1016/j.gie.2018.11.011. pmid:30452913