Clinical Endoscopy

  • 제57권1호
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  • Pages.11-17
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  • 2024
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  • 2234-2400(pISSN)
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  • 2234-2443(eISSN)
대한소화기내시경학회 (The Korean Society of Gastrointestinal Endoscopy)
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Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging

  • Yusuke Horiuchi (Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research) ;
  • Toshiaki Hirasawa (Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research) ;
  • Junko Fujisaki (Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research)
  • 투고 : 2023.07.08
  • 심사 : 2023.08.16
  • 발행 : 2024.01.30
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초록

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

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참고문헌

  1. Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin 2015;65:87-108.
  2. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394-424.
  3. Ferro A, Peleteiro B, Malvezzi M, et al. Worldwide trends in gastric cancer mortality (1980-2011), with predictions to 2015, and incidence by subtype. Eur J Cancer 2014;50:1330-1344.
  4. Ezoe Y, Muto M, Uedo N, et al. Magnifying narrowband imaging is more accurate than conventional white-light imaging in diagnosis of gastric mucosal cancer. Gastroenterology 2011;141:2017-2025.
  5. Yao K, Anagnostopoulos GK, Ragunath K. Magnifying endoscopy for diagnosing and delineating early gastric cancer. Endoscopy 2009;41:462-467.
  6. Muto M, Yao K, Kaise M, et al. Magnifying endoscopy simple diagnostic algorithm for early gastric cancer (MESDA-G). Dig Endosc 2016;28:379-393.
  7. Nakanishi H, Doyama H, Ishikawa H, et al. Evaluation of an e-learning system for diagnosis of gastric lesions using magnifying narrow-band imaging: a multicenter randomized controlled study. Endoscopy 2017;49:957-967.
  8. Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 2019;89:25-32.
  9. Shichijo S, Nomura S, Aoyama K, et al. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine 2017;25:106-111.
  10. Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018;21:653-660.
  11. Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc 2019;31:e34-e35.
  12. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012;1097-1105.
  13. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions [Internet]. Boston: IEEE Conference on Computer Vision and Pattern Recognition; 2015 [cited 2019 Mar 1]. Available from: https://arxiv.org/pdf/1409.4842.pdf.
  14. Xiao Z, Ji D, Li F, et al. Application of artificial intelligence in early gastric cancer diagnosis. Digestion 2022;103:69-75.
  15. Ochiai K, Ozawa T, Shibata J, et al. Current status of artificial intelligence-based computer-assisted diagnosis systems for gastric cancer in endoscopy. Diagnostics (Basel) 2022;12:3153.
  16. Chen PC, Lu YR, Kang YN, et al. The accuracy of artificial intelligence in the endoscopic diagnosis of early gastric cancer: pooled analysis study. J Med Internet Res 2022;24:e27694.
  17. Horiuchi Y, Aoyama K, Tokai Y, et al. Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging. Dig Dis Sci 2020;65:1355-1363.
  18. Horiuchi Y, Hirasawa T, Ishizuka N, et al. Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging (with videos). Gastrointest Endosc 2020;92:856-865.
  19. Ueyama H, Kato Y, Akazawa Y, et al. Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. J Gastroenterol Hepatol 2021;36:482-489.
  20. Li L, Chen Y, Shen Z, et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer 2020;23:126-132.
  21. Hu H, Gong L, Dong D, et al. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study. Gastrointest Endosc 2021;93:1333-1341.
  22. He X, Wu L, Dong Z, et al. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest Endosc 2022;95:671-678.
  23. Japanese Gastric Cancer Association. Japanese gastric cancer treatment guidelines 2021 (6th edition). Gastric Cancer 2023;26:1-25.
  24. Allum WH, Blazeby JM, Griffin SM, et al. Guidelines for the management of oesophageal and gastric cancer. Gut 2011;60:1449-1472.
  25. Thrumurthy SG, Chaudry MA, Hochhauser D, et al. The diagnosis and management of gastric cancer. BMJ 2013;347:f6367.
  26. Lee CK, Chung IK, Lee SH, et al. Is endoscopic forceps biopsy enough for a definitive diagnosis of gastric epithelial neoplasia? J Gastroenterol Hepatol 2010;25:1507-1513.
  27. Shim CN, Kim H, Kim DW, et al. Clinicopathologic factors and outcomes of histologic discrepancy between differentiated and undifferentiated types after endoscopic resection of early gastric cancer. Surg Endosc 2014;28:2097-2105.
  28. Takao M, Kakushima N, Takizawa K, et al. Discrepancies in histologic diagnoses of early gastric cancer between biopsy and endoscopic mucosal resection specimens. Gastric Cancer 2012;15:91-96.
  29. Lee JH, Kim JH, Rhee K, et al. Undifferentiated early gastric cancer diagnosed as differentiated histology based on forceps biopsy. Pathol Res Pract 2013;209:314-318.
  30. Horiuchi Y, Fujisaki J, Yamamoto N, et al. Pretreatment diagnosis factors associated with overtreatment with surgery in patients with differentiated-type early gastric cancer. Sci Rep 2019;9:15356.
  31. Yagi K, Nakamura A, Sekine A, et al. Magnifying endoscopy with narrow band imaging for early differentiated gastric adenocarcinoma. Dig Endosc 2008;20:115-122.
  32. Nakayoshi T, Tajiri H, Matsuda K, et al. Magnifying endoscopy combined with narrow band imaging system for early gastric cancer: correlation of vascular pattern with histopathology (including video). Endoscopy 2004;36:1080-1084.
  33. Yokoyama A, Inoue H, Minami H, et al. Novel narrow-band imaging magnifying endoscopic classification for early gastric cancer. Dig Liver Dis 2010;42:704-708.
  34. Yagi K, Sato T, Nakamura A, Sekine A. The possibility and limitation of magnifying endoscopic diagnosis using NBI in the extent of undifferentiated intramucosal gastric adenocarcinoma. Stomach Intest 2009;44:60-70.
  35. Okada K, Fujisaki J, Kasuga A, et al. Diagnosis of undifferentiated type early gastric cancers by magnification endoscopy with narrow-band imaging. J Gastroenterol Hepatol 2011;26:1262-1269.
  36. Horiuchi Y, Fujisaki J, Yamamoto N, et al. Accuracy of diagnostic demarcation of undifferentiated-type early gastric cancers for magnifying endoscopy with narrow-band imaging: endoscopic submucosal dissection cases. Gastric Cancer 2016;19:515-523.
  37. Horiuchi Y, Tokai Y, Yamamoto N, et al. Additive effect of magnifying endoscopy with narrow-band imaging for diagnosing mixed-type early gastric cancers. Dig Dis Sci 2020;65:591-599.
  38. Ling T, Wu L, Fu Y, 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.