Progress of artificial intelligence in endoscopic diagnosis of superficial esophageal squamous carcinoma and precancerous lesions_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

YAN Ping , YUAN Xianglei , ZHANG Qiongying ,  HU Bing

Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

HU Bing, Email: hubingnj@163.com

Keywords：

Esophageal squamous cell carcinoma; precancerous lesions; artificial intelligence; deep learning; digestive endoscopy; review

DOI：

10.7507/1007-4848.202203047

Video：

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Esophageal cancer is a serious threat to the health of Chinese people. The key to solve this problem is early diagnosis and early treatment, and the most important method is endoscopic screening. The rapid development of artificial intelligence (AI) technology makes its application and research in the field of digestive endoscopy growing, and it is expected to become the "right-hand man" for endoscopists in the early diagnosis of esophageal cancer. Currently, the application of multimodal and multifunctional AI systems has achieved good performance in the diagnosis of superficial esophageal squamous cell carcinoma and precancerous lesions. This study summarized and reviewed the research progress of AI in the diagnosis of superficial esophageal squamous cell carcinoma and precancerous lesions, and also explored its development direction in the future.

Citation： YAN Ping, YUAN Xianglei, ZHANG Qiongying, HU Bing. Progress of artificial intelligence in endoscopic diagnosis of superficial esophageal squamous carcinoma and precancerous lesions. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1217-1222. doi: 10.7507/1007-4848.202203047 Copy

1.	郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2019, 41(1): 19-28.
2.	Sung H, Ferlay J, Siegel RL, 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(3): 209-249.
3.	Abnet CC, Arnold M, Wei WQ. Epidemiology of esophageal squamous cell carcinoma. Gastroenterology, 2018, 154(2): 360-373.
4.	Chen XX, Zhong Q, Liu Y, et al. Genomic comparison of esophageal squamous cell carcinoma and its precursor lesions by multi-region whole-exome sequencing. Nat Commun, 2017, 8(1): 524.
5.	Naveed M, Kubiliun N. Endoscopic treatment of early-stage esophageal cancer. Curr Oncol Rep, 2018, 20(9): 71.
6.	Chen W, Zheng R, Zhang S, et al. Cancer incidence and mortality in China, 2013. Cancer Lett, 2017, 401: 63-71.
7.	Kriesel D. A brief introduction on neural networks. http://www.dkriesel.com.
8.	Holmes JH, Sacchi L, Bellazzi R, et al. Artificial intelligence in medicine AIME 2015. Artif Intell Med, 2017, 81: 1-2.
9.	吴玉超, 林岚, 王婧璇. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
10.	Cai SL, Li B, Tan WM, et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc, 2019, 90(5): 745-753.
11.	Tang D, Wang L, Jiang J, et al. A novel deep learning system for diagnosing early esophageal squamous cell carcinoma: A multicenter diagnostic study. Clin Transl Gastroenterol, 2021, 12(8): e00393.
12.	Li B, Cai SL, Tan WM, et al. Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging. World J Gastroenterol, 2021, 27(3): 281-293.
13.	Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc, 2019, 89(1): 25-32.
14.	Ohmori M, Ishihara R, Aoyama K, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc, 2020, 91(2): 301-309.
15.	Yang XX, Li Z, Shao XJ, et al. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig Endosc, 2021, 33(7): 1075-1084.
16.	Yuan XL, Guo LJ, Liu W, et al. Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study. J Gastroenterol Hepatol, 2022, 37(1): 169-178.
17.	Ikenoyama Y, Yoshio T, Tokura J, et al. Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma. Endoscopy, 2021, 53(11): 1105-1113.
18.	Fukuda H, Ishihara R, Kato Y, et al. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video). Gastrointest Endosc, 2020, 92(4): 848-855.
19.	Shiroma S, Yoshio T, Kato Y, et al. Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance. Sci Rep, 2021, 11(1): 7759.
20.	Waki K, Ishihara R, Kato Y, et al. Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation. Dig Endosc, 2021, 33(7): 1101-1109.
21.	Kumagai Y, Takubo K, Kawada K, et al. Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus. Esophagus, 2019, 16(2): 180-187.
22.	Natsugoe S, Baba M, Yoshinaka H, et al. Mucosal squamous cell carcinoma of the esophagus: A clinicopathologic study of 30 cases. Oncology, 1998, 55(3): 235-241.
23.	Tajima Y, Nakanishi Y, Tachimori Y, et al. Significance of involvement by squamous cell carcinoma of the ducts of esophageal submucosal glands. Analysis of 201 surgically resected superficial squamous cell carcinomas. Cancer, 2000, 89(2): 248-254.
24.	Bollschweiler E, Baldus SE, Schröder W, et al. High rate of lymph-node metastasis in submucosal esophageal squamous-cell carcinomas and adenocarcinomas. Endoscopy, 2006, 38(2): 149-156.
25.	Higuchi K, Koizumi W, Tanabe S, et al. Current management of esophageal squamous-cell carcinoma in Japan and other countries. Gastrointest Cancer Res, 2009, 3(4): 153-161.
26.	Ishihara R, Arima M, Iizuka T, et al. Endoscopic submucosal dissection/endoscopic mucosal resection guidelines for esophageal cancer. Dig Endosc, 2020, 32(4): 452-493.
27.	Tokai Y, Yoshio T, Aoyama K, et al. Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma. Esophagus, 2020, 17(3): 250-256.
28.	Nakagawa K, Ishihara R, Aoyama K, et al. Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointest Endosc, 2019, 90(3): 407-414.
29.	Wang YK, Syu HY, Chen YH, et al. Endoscopic images by a single-shot multibox detector for the identification of early cancerous lesions in the esophagus: A pilot study. Cancers (Basel), 2021, 13(2): 321.
30.	Shimamoto Y, Ishihara R, Kato Y, et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol, 2020, 55(11): 1037-1045.
31.	Oyama T, Inoue H, Arima M, et al. Prediction of the invasion depth of superficial squamous cell carcinoma based on microvessel morphology: Magnifying endoscopic classification of the Japan Esophageal Society. Esophagus, 2017, 14(2): 105-112.
32.	Mizumoto T, Hiyama T, Quach DT, et al. Magnifying endoscopy with narrow band imaging in estimating the invasion depth of superficial esophageal squamous cell carcinomas. Digestion, 2018, 98(4): 249-256.
33.	Everson M, Herrera L, Li W, et al. Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study. United European Gastroenterol J, 2019, 7(2): 297-306.
34.	Everson MA, Garcia-Peraza-Herrera L, Wang HP, et al. A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: Comparing diagnostic performance with a panel of expert European and Asian endoscopists. Gastrointest Endosc, 2021, 94(2): 273-281.
35.	Zhao YY, Xue DX, Wang YL, et al. Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy. Endoscopy, 2019, 51(4): 333-341.
36.	Uema R, Hayashi Y, Tashiro T, et al. Use of a convolutional neural network for classifying microvessels of superficial esophageal squamous cell carcinomas. J Gastroenterol Hepatol, 2021, 36(8): 2239-2246.
37.	Guo L, Xiao X, Wu C, et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc, 2020, 91(1): 41-51.
38.	Liu W, Yuan X, Guo L, et al. Artificial intelligence for detecting and delineating margins of early ESCC under WLI endoscopy. Clin Transl Gastroenterol, 2022, 13(1): e00433.

1. 郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2019, 41(1): 19-28.
2. Sung H, Ferlay J, Siegel RL, 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(3): 209-249.
3. Abnet CC, Arnold M, Wei WQ. Epidemiology of esophageal squamous cell carcinoma. Gastroenterology, 2018, 154(2): 360-373.
4. Chen XX, Zhong Q, Liu Y, et al. Genomic comparison of esophageal squamous cell carcinoma and its precursor lesions by multi-region whole-exome sequencing. Nat Commun, 2017, 8(1): 524.
5. Naveed M, Kubiliun N. Endoscopic treatment of early-stage esophageal cancer. Curr Oncol Rep, 2018, 20(9): 71.
6. Chen W, Zheng R, Zhang S, et al. Cancer incidence and mortality in China, 2013. Cancer Lett, 2017, 401: 63-71.
7. Kriesel D. A brief introduction on neural networks. http://www.dkriesel.com.
8. Holmes JH, Sacchi L, Bellazzi R, et al. Artificial intelligence in medicine AIME 2015. Artif Intell Med, 2017, 81: 1-2.
9. 吴玉超, 林岚, 王婧璇. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
10. Cai SL, Li B, Tan WM, et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc, 2019, 90(5): 745-753.
11. Tang D, Wang L, Jiang J, et al. A novel deep learning system for diagnosing early esophageal squamous cell carcinoma: A multicenter diagnostic study. Clin Transl Gastroenterol, 2021, 12(8): e00393.
12. Li B, Cai SL, Tan WM, et al. Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging. World J Gastroenterol, 2021, 27(3): 281-293.
13. Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc, 2019, 89(1): 25-32.
14. Ohmori M, Ishihara R, Aoyama K, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc, 2020, 91(2): 301-309.
15. Yang XX, Li Z, Shao XJ, et al. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig Endosc, 2021, 33(7): 1075-1084.
16. Yuan XL, Guo LJ, Liu W, et al. Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study. J Gastroenterol Hepatol, 2022, 37(1): 169-178.
17. Ikenoyama Y, Yoshio T, Tokura J, et al. Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma. Endoscopy, 2021, 53(11): 1105-1113.
18. Fukuda H, Ishihara R, Kato Y, et al. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video). Gastrointest Endosc, 2020, 92(4): 848-855.
19. Shiroma S, Yoshio T, Kato Y, et al. Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance. Sci Rep, 2021, 11(1): 7759.
20. Waki K, Ishihara R, Kato Y, et al. Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation. Dig Endosc, 2021, 33(7): 1101-1109.
21. Kumagai Y, Takubo K, Kawada K, et al. Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus. Esophagus, 2019, 16(2): 180-187.
22. Natsugoe S, Baba M, Yoshinaka H, et al. Mucosal squamous cell carcinoma of the esophagus: A clinicopathologic study of 30 cases. Oncology, 1998, 55(3): 235-241.
23. Tajima Y, Nakanishi Y, Tachimori Y, et al. Significance of involvement by squamous cell carcinoma of the ducts of esophageal submucosal glands. Analysis of 201 surgically resected superficial squamous cell carcinomas. Cancer, 2000, 89(2): 248-254.
24. Bollschweiler E, Baldus SE, Schröder W, et al. High rate of lymph-node metastasis in submucosal esophageal squamous-cell carcinomas and adenocarcinomas. Endoscopy, 2006, 38(2): 149-156.
25. Higuchi K, Koizumi W, Tanabe S, et al. Current management of esophageal squamous-cell carcinoma in Japan and other countries. Gastrointest Cancer Res, 2009, 3(4): 153-161.
26. Ishihara R, Arima M, Iizuka T, et al. Endoscopic submucosal dissection/endoscopic mucosal resection guidelines for esophageal cancer. Dig Endosc, 2020, 32(4): 452-493.
27. Tokai Y, Yoshio T, Aoyama K, et al. Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma. Esophagus, 2020, 17(3): 250-256.
28. Nakagawa K, Ishihara R, Aoyama K, et al. Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointest Endosc, 2019, 90(3): 407-414.
29. Wang YK, Syu HY, Chen YH, et al. Endoscopic images by a single-shot multibox detector for the identification of early cancerous lesions in the esophagus: A pilot study. Cancers (Basel), 2021, 13(2): 321.
30. Shimamoto Y, Ishihara R, Kato Y, et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol, 2020, 55(11): 1037-1045.
31. Oyama T, Inoue H, Arima M, et al. Prediction of the invasion depth of superficial squamous cell carcinoma based on microvessel morphology: Magnifying endoscopic classification of the Japan Esophageal Society. Esophagus, 2017, 14(2): 105-112.
32. Mizumoto T, Hiyama T, Quach DT, et al. Magnifying endoscopy with narrow band imaging in estimating the invasion depth of superficial esophageal squamous cell carcinomas. Digestion, 2018, 98(4): 249-256.
33. Everson M, Herrera L, Li W, et al. Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study. United European Gastroenterol J, 2019, 7(2): 297-306.
34. Everson MA, Garcia-Peraza-Herrera L, Wang HP, et al. A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: Comparing diagnostic performance with a panel of expert European and Asian endoscopists. Gastrointest Endosc, 2021, 94(2): 273-281.
35. Zhao YY, Xue DX, Wang YL, et al. Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy. Endoscopy, 2019, 51(4): 333-341.
36. Uema R, Hayashi Y, Tashiro T, et al. Use of a convolutional neural network for classifying microvessels of superficial esophageal squamous cell carcinomas. J Gastroenterol Hepatol, 2021, 36(8): 2239-2246.
37. Guo L, Xiao X, Wu C, et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc, 2020, 91(1): 41-51.
38. Liu W, Yuan X, Guo L, et al. Artificial intelligence for detecting and delineating margins of early ESCC under WLI endoscopy. Clin Transl Gastroenterol, 2022, 13(1): e00433.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Progress of artificial intelligence in endoscopic diagnosis of superficial esophageal squamous carcinoma and precancerous lesions

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