Accuracy of endoscopy-based artificial intelligence-assisted diagnostic system in the diagnosis of early esophageal cancer: A systematic review and meta-analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

HONG Ziqiang ^1,2 , JIN Dacheng ² , LI Hongchao ^1,2 , CHENG Tao ^1,2 , BAI Xiangdou ^1,2 , WU Xusheng ^1,2 , CUI Baiqiang ^1,2 ,  GOU Yunjiu ²

1. The First Clinical Department of Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, P. R. China;
2. Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, 730000, P. R. China;

Corresponding?author：

GOU Yunjiu, Email: gouyunjiu@163.com

Keywords：

Artificial intelligence; endoscopy; early esophageal cancer; diagnosis; systematic review/meta-analysis

DOI：

10.7507/1007-4848.202204033

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To systematically evaluate the accuracy of endoscopy-based artificial intelligence (AI)-assisted diagnostic systems in the diagnosis of early-stage esophageal cancer and provide a scientific basis for its diagnostic value. Methods PubMed, EMbase, The Cochrane Library, Web of Science, Wanfang database, VIP database and CNKI database were searched by computer to search for the relevant literature about endoscopy-based AI-assisted diagnostic systems for the diagnosis of early esophageal cancer from inception to March 2022. The QUADAS-2 was used for quality evaluation of included studies. Meta-analysis of the literature was carried out using Stata 16, Meta-Disc 1.4 and RevMan 5.4 softwares. A bivariate mixed effects regression model was utilized to calculate the combined diagnostic efficacy of the AI-assisted system and meta-regression analysis was conducted to explore the sources of heterogeneity. Results A total of 17 articles were included, which consisted of 13 retrospective cohort studies and 4 prospective cohort studies. The results of the quality evaluation using QUADAS-2 showed that all included literature was of high quality. The obtained meta-analysis results revealed that the AI-assisted system in the diagnosis of esophageal cancer presented a combined sensitivity of 0.94 (95%CI 0.91 to 0.96), a specificity of 0.85 (95%CI 0.74 to 0.92), a positive likelihood ratio of 6.28 (95%CI 3.48 to 11.33), a negative likelihood ratio of 0.07 (95%CI 0.05 to 0.11), a diagnostic odds ratio of 89 (95%CI 38 to 208) and an area under the curve of 0.96 (95%CI 0.94 to 0.98). Conclusion The AI-assisted diagnostic system has a high diagnostic value for early stage esophageal cancer. However, most of the included studies were retrospective. Therefore, further high-quality prospective studies are needed for validation.

Citation： HONG Ziqiang, JIN Dacheng, LI Hongchao, CHENG Tao, BAI Xiangdou, WU Xusheng, CUI Baiqiang, GOU Yunjiu. Accuracy of endoscopy-based artificial intelligence-assisted diagnostic system in the diagnosis of early esophageal cancer: A systematic review and meta-analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(9): 1329-1336. doi: 10.7507/1007-4848.202204033 Copy

1.	Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin, 2015, 65(2): 87-108.
2.	Lin Y, Totsuka Y, Shan B, et al. Esophageal cancer in high-risk areas of China: Research progress and challenges. Ann Epidemiol, 2017, 27(3): 215-221.
3.	Pennathur A, Gibson MK, Jobe BA, et al. Oesophageal carcinoma. Lancet, 2013, 381(9864): 400-412.
4.	李珊, 陳霖, 張宇航, 等. 早期食管癌: 內鏡還是外科手術? 中國胸心血管外科臨床雜志, 2020, 27(10): 1223-1227.
5.	Thakkar SJ, Kochhar GS. Artificial intelligence for real-time detection of early esophageal cancer: Another set of eyes to better visualize. Gastrointest Endosc, 2020, 91(1): 52-54.
6.	de Souza LA, Palm C, Mendel R, et al. A survey on Barrett's esophagus analysis using machine learning. Comput Biol Med, 2018, 96: 203-213.
7.	Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
8.	Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pat Recog, 1997, 30(7): 1145-1159.
9.	Noma H, Matsushima Y, Ishii R. Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. Commun Stat Case Stud Data Anal Appl, 2021, 7: 344-358.
10.	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.
11.	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.
12.	de Groof J, van der Sommen F, van der Putten J, et al. The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. United European Gastroenterol J, 2019, 7(4): 538-547.
13.	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.
14.	Fonollà R, Scheeve T, Struyvenberg MR, et al. Ensemble of deep con-volutional neural networks for classification of early Barrett's neopla-sia using volumetric laser endomicroscopy. Appl Sci, 2019, 9: 2183.
15.	石善江, 王宏光, 劉時助, 等. 應用卷積神經網絡的人工智能技術在早期食管癌診斷中的臨床分析. 中外醫療, 2019, 38(18): 7-9, 16.
16.	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.
17.	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.
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.	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.
20.	Liu G, Hua J, Wu Z, et al. Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network. Ann Transl Med, 2020, 8(7): 486.
21.	Hashimoto R, Requa J, Dao T, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). Gastrointest Endosc, 2020, 91(6): 1264-1271.
22.	de Groof AJ, Struyvenberg MR, Fockens KN, et al. Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: A pilot study (with video). Gastrointest Endosc, 2020, 91(6): 1242-1250.
23.	de Groof AJ, Struyvenberg MR, van der Putten J, et al. Deep-learning system detects neoplasia in patients with Barrett's esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology, 2020, 158(4): 915-929.
24.	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.
25.	Ebigbo A, Mendel R, Probst A, et al. Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus. Gut, 2020, 69(4): 615-616.
26.	Iwagami H, Ishihara R, Aoyama K, et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol, 2021, 36(1): 131-136.
27.	Wei WQ, Chen ZF, He YT, et al. Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in China. J Clin Oncol, 2015, 33(17): 1951-1957.
28.	Mehrer J, Spoerer CJ, Jones EC, et al. An ecologically motivated image dataset for deep learning yields better models of human vision. Proc Natl Acad Sci U S A, 2021, 118(8): e2011417118.
29.	付一鳴, 劉曉燕, 韓澤龍, 等. 人工智能輔助內鏡在消化道早癌篩查應用研究進展. 中華消化內鏡雜志, 2019, 36(4): 296-299.
30.	王智杰, 高杰, 孟茜茜, 等. 基于深度學習的人工智能技術在早期胃癌診斷中的應用. 中華消化內鏡雜志, 2018, 35(8): 551-556.
31.	李幼平, 主編. 實用循證醫學. 北京: 人民衛生出版社, 2018.
32.	中華醫學會消化內鏡學分會消化系早癌內鏡診斷與治療協作組, 中華醫學會消化病學分會消化道腫瘤協作組, 中華醫學會消化病學分會消化病理學組. 中國早期食管鱗狀細胞癌及癌前病變篩查與診治共識(2015年, 北京). 中華消化內鏡雜志, 2016, 33(1): 3-18.
33.	Akutsu Y, Uesato M, Shuto K, et al. The overall prevalence of metastasis in T1 esophageal squamous cell carcinoma: A retrospective analysis of 295 patients. Ann Surg, 2013, 257(6): 1032-1038.
34.	Kitagawa Y, Uno T, Oyama T, et al. Esophageal cancer practice guidelines 2017 edited by the Japan esophageal society: Part 2. Esophagus, 2019, 16(1): 25-43.
35.	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.

1. Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin, 2015, 65(2): 87-108.
2. Lin Y, Totsuka Y, Shan B, et al. Esophageal cancer in high-risk areas of China: Research progress and challenges. Ann Epidemiol, 2017, 27(3): 215-221.
3. Pennathur A, Gibson MK, Jobe BA, et al. Oesophageal carcinoma. Lancet, 2013, 381(9864): 400-412.
4. 李珊, 陳霖, 張宇航, 等. 早期食管癌: 內鏡還是外科手術? 中國胸心血管外科臨床雜志, 2020, 27(10): 1223-1227.
5. Thakkar SJ, Kochhar GS. Artificial intelligence for real-time detection of early esophageal cancer: Another set of eyes to better visualize. Gastrointest Endosc, 2020, 91(1): 52-54.
6. de Souza LA, Palm C, Mendel R, et al. A survey on Barrett's esophagus analysis using machine learning. Comput Biol Med, 2018, 96: 203-213.
7. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
8. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pat Recog, 1997, 30(7): 1145-1159.
9. Noma H, Matsushima Y, Ishii R. Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies. Commun Stat Case Stud Data Anal Appl, 2021, 7: 344-358.
10. 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.
11. 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.
12. de Groof J, van der Sommen F, van der Putten J, et al. The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. United European Gastroenterol J, 2019, 7(4): 538-547.
13. 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.
14. Fonollà R, Scheeve T, Struyvenberg MR, et al. Ensemble of deep con-volutional neural networks for classification of early Barrett's neopla-sia using volumetric laser endomicroscopy. Appl Sci, 2019, 9: 2183.
15. 石善江, 王宏光, 劉時助, 等. 應用卷積神經網絡的人工智能技術在早期食管癌診斷中的臨床分析. 中外醫療, 2019, 38(18): 7-9, 16.
16. 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.
17. 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.
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. 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.
20. Liu G, Hua J, Wu Z, et al. Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network. Ann Transl Med, 2020, 8(7): 486.
21. Hashimoto R, Requa J, Dao T, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). Gastrointest Endosc, 2020, 91(6): 1264-1271.
22. de Groof AJ, Struyvenberg MR, Fockens KN, et al. Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: A pilot study (with video). Gastrointest Endosc, 2020, 91(6): 1242-1250.
23. de Groof AJ, Struyvenberg MR, van der Putten J, et al. Deep-learning system detects neoplasia in patients with Barrett's esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology, 2020, 158(4): 915-929.
24. 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.
25. Ebigbo A, Mendel R, Probst A, et al. Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus. Gut, 2020, 69(4): 615-616.
26. Iwagami H, Ishihara R, Aoyama K, et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol, 2021, 36(1): 131-136.
27. Wei WQ, Chen ZF, He YT, et al. Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in China. J Clin Oncol, 2015, 33(17): 1951-1957.
28. Mehrer J, Spoerer CJ, Jones EC, et al. An ecologically motivated image dataset for deep learning yields better models of human vision. Proc Natl Acad Sci U S A, 2021, 118(8): e2011417118.
29. 付一鳴, 劉曉燕, 韓澤龍, 等. 人工智能輔助內鏡在消化道早癌篩查應用研究進展. 中華消化內鏡雜志, 2019, 36(4): 296-299.
30. 王智杰, 高杰, 孟茜茜, 等. 基于深度學習的人工智能技術在早期胃癌診斷中的應用. 中華消化內鏡雜志, 2018, 35(8): 551-556.
31. 李幼平, 主編. 實用循證醫學. 北京: 人民衛生出版社, 2018.
32. 中華醫學會消化內鏡學分會消化系早癌內鏡診斷與治療協作組, 中華醫學會消化病學分會消化道腫瘤協作組, 中華醫學會消化病學分會消化病理學組. 中國早期食管鱗狀細胞癌及癌前病變篩查與診治共識(2015年, 北京). 中華消化內鏡雜志, 2016, 33(1): 3-18.
33. Akutsu Y, Uesato M, Shuto K, et al. The overall prevalence of metastasis in T1 esophageal squamous cell carcinoma: A retrospective analysis of 295 patients. Ann Surg, 2013, 257(6): 1032-1038.
34. Kitagawa Y, Uno T, Oyama T, et al. Esophageal cancer practice guidelines 2017 edited by the Japan esophageal society: Part 2. Esophagus, 2019, 16(1): 25-43.
35. 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.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Accuracy of endoscopy-based artificial intelligence-assisted diagnostic system in the diagnosis of early esophageal cancer: A systematic review and meta-analysis

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