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The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review

  • JunHo Lee (Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center) ;
  • Hanna Lee (Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center) ;
  • Jun-won Chung (Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center)
  • Received : 2023.07.07
  • Accepted : 2023.07.31
  • Published : 2023.07.31

Abstract

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

Keywords

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209-249. https://doi.org/10.3322/caac.21660
  2. Lee HA, Lee TY, Kim YR. Comparative analysis of stomach cancer stages and related factors according to the diagnosis path. J Korea Acad Ind Coop Soc 2015;16:2656-2664.
  3. Jung DH. Endoscopic resection of early gastric cancer in elderly. Korean J Gastroenterol 2022;80:1-5. https://doi.org/10.4166/kjg.2022.084
  4. Hamashima C, Okamoto M, Shabana M, Osaki Y, Kishimoto T. Sensitivity of endoscopic screening for gastric cancer by the incidence method. Int J Cancer 2013;133:653-659. https://doi.org/10.1002/ijc.28065
  5. Ren W, Yu J, Zhang ZM, Song YK, Li YH, Wang L. Missed diagnosis of early gastric cancer or high-grade intraepithelial neoplasia. World J Gastroenterol 2013;19:2092-2096. https://doi.org/10.3748/wjg.v19.i13.2092
  6. Yao K. The endoscopic diagnosis of early gastric cancer. Ann Gastroenterol 2013;26:11-22.
  7. Pimenta-Melo AR, Monteiro-Soares M, Libanio D, Dinis-Ribeiro M. Missing rate for gastric cancer during upper gastrointestinal endoscopy: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol 2016;28:1041-1049. https://doi.org/10.1097/MEG.0000000000000657
  8. Zhang YH, Guo LJ, Yuan XL, Hu B. Artificial intelligence-assisted esophageal cancer management: now and future. World J Gastroenterol 2020;26:5256-5271. https://doi.org/10.3748/wjg.v26.i35.5256
  9. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med 2018;284:603-619. https://doi.org/10.1111/joim.12822
  10. Albelwi S, Mahmood A. A framework for designing the architectures of deep convolutional neural networks. Entropy (Basel) 2017;19:242.
  11. Miyaki R, Yoshida S, Tanaka S, Kominami Y, Sanomura Y, Matsuo T, et al. A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer. J Clin Gastroenterol 2015;49:108-115. https://doi.org/10.1097/MCG.0000000000000104
  12. Cho BJ, Bang CS, Park SW, Yang YJ, Seo SI, Lim H, et al. Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network. Endoscopy 2019;51:1121-1129. https://doi.org/10.1055/a-0981-6133
  13. Ikenoyama Y, Hirasawa T, Ishioka M, Namikawa K, Yoshimizu S, Horiuchi Y, et al. Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists. Dig Endosc 2021;33:141-150. https://doi.org/10.1111/den.13688
  14. Ishioka M, Osawa H, Hirasawa T, Kawachi H, Nakano K, Fukushima N, et al. Performance of an artificial intelligence-based diagnostic support tool for early gastric cancers: Retrospective study. Dig Endosc 2023;35:483-491. https://doi.org/10.1111/den.14455
  15. Lui TK, Wong KK, Mak LL, To EW, Tsui VW, Deng Z, et al. Feedback from artificial intelligence improved the learning of junior endoscopists on histology prediction of gastric lesions. Endosc Int Open 2020;8:E139-E146. https://doi.org/10.1055/a-1036-6114
  16. Ono H, Yao K, Fujishiro M, Oda I, Uedo N, Nimura S, et al. Guidelines for endoscopic submucosal dissection and endoscopic mucosal resection for early gastric cancer (second edition). Dig Endosc 2021;33:4-20. https://doi.org/10.1111/den.13883
  17. Messmann H, Bisschops R, Antonelli G, Libanio D, Sinonquel P, Abdelrahim M, et al. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022;54:1211-1231. https://doi.org/10.1055/a-1950-5694
  18. An P, Yang D, Wang J, Wu L, Zhou J, Zeng Z, et al. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer 2020;23:884-892. https://doi.org/10.1007/s10120-020-01071-7
  19. Ling T, Wu L, Fu Y, Xu Q, An P, Zhang J, et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy 2021;53:469-477. https://doi.org/10.1055/a-1229-0920
  20. Liu L, Dong Z, Cheng J, Bu X, Qiu K, Yang C, et al. Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study. Front Oncol 2023;12:1075578.
  21. Hu H, Gong L, Dong D, Zhu L, Wang M, He J, et al. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study. Gastrointest Endosc 2021;93:1333-1341.e3. https://doi.org/10.1016/j.gie.2020.11.014
  22. Nagao S, Tsuji Y, Sakaguchi Y, Takahashi Y, Minatsuki C, Niimi K, et al. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. Gastrointest Endosc 2020;92:866-873.e1. https://doi.org/10.1016/j.gie.2020.06.047
  23. Zhu Y, Wang QC, Xu MD, Zhang Z, Cheng J, Zhong YS, et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc 2019;89:806-815.e1. https://doi.org/10.1016/j.gie.2018.11.011
  24. Nam JY, Chung HJ, Choi KS, Lee H, Kim TJ, Soh H, et al. Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison. Gastrointest Endosc 2022;95:258-268.e10. https://doi.org/10.1016/j.gie.2021.08.022
  25. Hisada H, Sakaguchi Y, Oshio K, Mizutani S, Nakagawa H, Sato J, et al. Endoscopic treatment of superficial gastric cancer: present status and future. Curr Oncol 2022;29:4678-4688. https://doi.org/10.3390/curroncol29070371
  26. Choi J, Kim SG, Im JP, Kim JS, Jung HC, Song IS. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy 2010;42:705-713. https://doi.org/10.1055/s-0030-1255617
  27. Yanai H, Noguchi T, Mizumachi S, Tokiyama H, Nakamura H, Tada M, et al. A blind comparison of the effectiveness of endoscopic ultrasonography and endoscopy in staging early gastric cancer. Gut 1999;44:361-365. https://doi.org/10.1136/gut.44.3.361
  28. Yoon HJ, Kim S, Kim JH, Keum JS, Oh SI, Jo J, et al. A lesion-based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J Clin Med 2019;8:1310.
  29. Wu L, Wang J, He X, Zhu Y, Jiang X, Chen Y, et al. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointest Endosc 2022;95:92-104.e3. https://doi.org/10.1016/j.gie.2021.06.033
  30. Wu L, He X, Liu M, Xie H, An P, Zhang J, et al. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial. Endoscopy 2021;53:1199-1207. https://doi.org/10.1055/a-1350-5583
  31. Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, et al. The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin 2017;67:93-99. https://doi.org/10.3322/caac.21388
  32. Jin C, Jiang Y, Yu H, Wang W, Li B, Chen C, et al. Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. Br J Surg 2021;108:542-549. https://doi.org/10.1002/bjs.11928
  33. Li J, Dong D, Fang M, Wang R, Tian J, Li H, et al. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol 2020;30:2324-2333. https://doi.org/10.1007/s00330-019-06621-x
  34. Dong D, Fang MJ, Tang L, Shan XH, Gao JB, Giganti F, et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol 2020;31:912-920. https://doi.org/10.1016/j.annonc.2020.04.003
  35. Jiang Y, Jin C, Yu H, Wu J, Chen C, Yuan Q, et al. Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter, retrospective study. Ann Surg 2021;274:e1153-e1161. https://doi.org/10.1097/SLA.0000000000003778
  36. Zhang L, Dong D, Zhang W, Hao X, Fang M, Wang S, et al. A deep learning risk prediction model for overall survival in patients with gastric cancer: a multicenter study. Radiother Oncol 2020;150:73-80. https://doi.org/10.1016/j.radonc.2020.06.010