Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice

Abstract

Background Artificial intelligence (AI) has made remarkable progress in image recognition using deep learning systems. It has been used to detect esophageal squamous cell carcinoma (ESCC); however, none of the previous reports were investigations in a clinical setting, being retrospective in design. We therefore conducted this trial to determine how AI can help endoscopists detect ESCC in a clinical setting.

Methods This was a prospective, single-center, exploratory, and randomized controlled trial. High risk patients with ESCC undergoing screening or surveillance esophagogastroduodenoscopy were enrolled and randomly assigned to either the AI or control groups. In the AI group, the endoscopists watched both the AI monitor that detected ESCC with annotation and the normal monitor simultaneously; in the control group, the endoscopists watched only the normal monitor. In both groups, the endoscopists observed the esophagus using white-light imaging (WLI), followed by narrow-band imaging (NBI), then iodine staining. The primary end point was the enhanced detection rate of ESCC by nonexperts using AI. The detection rate was defined as the ratio of WLI/NBI-detected ESCCs to all ESCCs detected by iodine staining.

Results 320 patients were included in the analysis. The detection rate of ESCC among nonexperts was 47% in the AI group and 45% in the control group (P = 0.93), with no significant difference, which was similarly found for experts (87% vs. 57%; P = 0.20) and all endoscopists (57% vs. 50%; P = 0.70).

Conclusions This study could not demonstrate an improvement in the esophageal cancer detection rate using the AI diagnostic support system for ESCC.

Publication History

Received: 22 February 2024

Accepted after revision: 24 September 2024

Accepted Manuscript online:
24 September 2024

Article published online:
18 November 2024

• References

• 1 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: 209-249
• 2 Morgan E, Soerjomataram I, Rumgay H. et al. The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040: New estimates from GLOBOCAN 2020. Gastroenterology 2022; 163: 649-658.e2
• 3 Allemani C, Matsuda T, Di Carlo V. et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population based registries in 71 countries. Lancet 2018; 391: 1023-1075
• 4 Hashimoto CL, Iriya K, Baba ER. et al. Lugol’s dye spray chromoendoscopy establishes early diagnosis of esophageal cancer in patients with primary head and neck cancer. Am J Gastroenterol 2005; 100: 275-282
• 5 Dawsey SM, Fleischer DE, Wang GQ. et al. Mucosal iodine staining improves endoscopic visualization of squamous dysplasia and squamous cell carcinoma of the esophagus in Linxian, China. Cancer 1998; 83: 220-231
• 6 Naito S, Yoshio T, Ishiyama A. et al. Long-term outcomes of esophageal squamous cell carcinoma with invasion depth of pathological T1a-muscularis mucosae and T1b-submucosa by endoscopic resection followed by appropriate additional treatment. Dig Endosc 2022; 34: 793-804
• 7 Kaneko K, Oono Y, Yano T. et al. Effect of novel bright image enhanced endoscopy using blue laser imaging (BLI). Endosc Int Open 2014; 2: E212-E219
• 8 Morita FH, Bernardo WM, Ide E. et al. Narrow band imaging versus Lugol chromoendoscopy to diagnose squamous cell carcinoma of the esophagus: A systematic review and meta-analysis. BMC Cancer 2017; 17: 54
• 9 Ishihara R, Takeuchi Y, Chatani R. et al. Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists. Dis Esoph 2010; 23: 480-486
• 10 Ishihara R, Yamada T, Iishi H. et al. Quantitative analysis of the color change after iodine staining for diagnosing esophageal high-grade intraepithelial neoplasia and invasive cancer. Gastrointest Endosc 2009; 69: 213-218
• 11 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
• 12 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
• 13 Tamashiro A, Yoshio T, Ishiyama A. et al. Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks. Dig Endosc 2020; 32: 1057-1065
• 14 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: 250-256
• 15 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: 1105-1013
• 16 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: 7759
• 17 Yuan XL, Liu W, Lin YX. et al. Effect of an artificial intelligence-assisted system on endoscopic diagnosis of superficial oesophageal squamous cell carcinoma and precancerous lesions: a multicentre, tandem, double-blind, randomised controlled trial. Lancet Gastroenterol Hepatol 2024; 9: 34-44
• 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: 848-855
• 19 National Cancer Institute, U.S. Department of Health and Human Services. Common Terminology Criteria for Adverse Events (CTCAE) Version 5.0; 2017. https://ctep.cancer.gov/protocoldevelopment/electronic_applications/docs/CTCAE_v5_Quick_Reference_8.5×11.pdf
• 20 Bugter O, van de Ven SEM, Hardillo JA. et al. Early detection of esophageal second primary tumors using Lugol chromoendoscopy in patients with head and neck cancer: A systematic review and meta-analysis. Head Neck 2019; 41: 1122-1130
• 21 Yamashina T, Ishihara R, Nagai K. et al. Long-term outcome and metastatic risk after endoscopic resection of superficial esophageal squamous cell carcinoma. Am J Gastroenterol 2013; 108: 544-551
• 22 Nagami Y, Tominaga K, Machida H. et al. Usefulness of non-magnifying narrow-band imaging in screening of early esophageal squamous cell carcinoma: A prospective comparative study using propensity score matching. Am J Gastroenterol 2014; 109: 845-854
• 23 Takahashi M, Shimizu Y, Ono M. et al. Endoscopic diagnosis of early neoplasia of the esophagus with narrow band imaging: Correlations among background coloration and iodine staining findings. J Gastroenterol Hepatol 2014; 29: 762-768
• 24 Huang LY, Cui J, Wu CR. et al. Narrow-band imaging in the diagnosis of early esophageal cancer and precancerous lesions. Chin Med J (Engl) 2009; 122: 776-780
• 25 Kamba S, Tamai N, Saitoh I. et al. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: A multicenter randomized controlled trial. J Gastroenterol 2021; 56: 746-757