An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report

Jiejun  Lin; Xiao Tao; Jie Pan

doi:10.55976/jdh.1202214525-29

Home /

Archives /

Journal of Digital Health, Volume 1 Issue 1, 2022 /

Case Report

An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report

Jiejun Lin ¹^# , Xiao Tao ²^# , Jie Pan ³ ^* ^#

1. Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, Zhejiang Province, China
2. Wuhan University, Wuhan, Hubei Province, China
3. Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, Zhejiang Province, China

# Jiejun Lin, Xiao Tao, and Jie Pan contributed equally to this work

*Correspondence: 783202415@qq.com

DOI: https://doi.org/10.55976/jdh.1202214525-29

Received

13 December 2021
Revised

22 February 2022
Accepted

25 February 2022
Published

02 March 2022

Early gastric cancer Artificial intelligence Gastrointestinal endoscopy

Abstract

<p><strong style="font-size: 16">Background</strong><strong style="font-size: 16">: </strong>The mucosal changes of early gastric cancers (EGC) are slight and difficult to be recognized, leading to a high miss rate. Artificial intelligence (AI) systems have the potential to improve the detection of EGC. Here, we report a case of early gastric cancer discovered by an endoscopist with the assistance of an AI system. </p> <p><strong style="font-size: 16">Case presentation</strong><strong style="font-size: 16">: </strong>A 67-year-old male patient came to our hospital for Esophagogastroduodenoscopy (EGD) due to a routine physical examination. He had previously been healthy but was treated for a Helicobacter pylori infection two years ago. In the process of EGD, the AI system flagged a tiny mucosal lesion that was far away and was not detected by the endoscopist, and this lesion attracted the endoscopist's additional attention. After the close observation of the lesion, the AI system immediately gave a red prompt box, suggesting that the endoscopist further observe it. Under magnifying narrow-band imaging (ME-NBI), the mucosal glands and blood vessels of the lesion were irregular, and AI diagnosed it as a suspicious gastric carcinoma. Biopsy pathology showed that it was high-grade intraepithelial neoplasia, and after endoscopic mucosal dissection (ESD), post-ESD histology confirmed that the lesion was a highly differentiated adenocarcinoma confined to the mucosa, with a lesion range of 1.1cm × 1.0cm. The patient was discharged from the hospital without any postoperative complications.</p> <p><strong style="font-size: 16">Conclusion</strong><strong style="font-size: 16">: </strong>AI has been widely applied in the field of gastrointestinal endoscopy and has the potential to help improve the detection rate of early gastrointestinal cancers. We reported a case of early gastric cancer discovered by the endoscopist with the assistance of AI.</p>

<h5>Introduction</h5>
<p>Gastric cancer (GC) is the fifth most common cancer in the world and one of the leading causes of cancer-related mortality. In 2020, approximately 1.1 million people were diagnosed with gastric cancer and 770,000 people died from it <a href="#reference_list_0">[1]</a>. Gastric cancer has a high mortality rate as a result of being diagnosed at an advanced stage. In most parts of the world, the five-year survival rate of advanced gastric cancer patients is only 20%, while the five-year survival rate of early gastric cancer (EGC) patients can be as high as 90% <a href="#reference_list_1">[2]</a>. Therefore, the timely detection of early gastric cancer and its subsequent treatment can significantly reduce the mortality rate and save the lives of patients.</p>
<p>Esophagogastroduodenoscopy (EGD) is the main strategy to detect gastric mucosal lesions. White light endoscopy (WLE) is the main tool for early cancer screening. However, early gastric cancer mostly has no obvious symptoms, and the endoscopic features of mucosal lesions are subtle and can be missed. In addition, the quality of endoscopy depends on the experience of the endoscopist, but the level of endoscopists is uneven, so there is a high rate of missed diagnosis of early gastric cancer <a href="#reference_list_2">[3]</a>. How to improve the detection rate of gastric cancer is a serious problem we are facing.</p>
<p>In recent years, artificial intelligence (AI) technology has become a research hotspot in the medical field, showing great potential in gastrointestinal endoscopy. ENDOANGEL is an artificial intelligence system that can monitor blind spots, detect EGCs in EGD in real-time and make preliminary predictions about the depth of invasion and differentiation status of EGC. This system has been reported in detail in previous studies<a href="#reference_list_3">[4-7]</a>. This paper reports a case of early gastric carcinoma discovered by an AI system named ENDOANGEL.</p>
<h5 class="journalThemeColor">Case presentation</h5>
<p>A 67-year-old Chinese male patient came to Wenzhou Central Hospital for EGD due to routine physical examination. He had previously been healthy but was treated for a Helicobacter pylori infection two years ago. He said that he had no obvious physical discomfort and the physical examination did not reveal any abnormal findings. But his father died of gastric cancer. In the process of EGD, the AI system flagged a tiny mucosal lesion that was far away and was not detected by the endoscopist, which attracted the endoscopist's additional attention. Then, the lesion was carefully observed at a close distance. The lesion was located in the posterior wall of the gastric antrum near the pylorus, with slight uplift and slight yellow and granular surface, which was classified as type 0-IIa according to the Paris classification (Figure 1). ENDOANGEL then gave a red prompt box and suggested the endoscopist for further observation under narrow-band imaging (ME-NBI). When the lesion was carefully viewed under ME-NBI, it was found that there was a clear demarcation line (DL) around the lesion with irregular glands and microvessels varied in diameter and size, which were irregular in shape (Figure 2), and ENDOANGEL diagnosed it as gastric neoplasm. Based on the above, a highly differentiated adenocarcinoma was suspected, and biopsy histopathology confirmed that it was high-grade intraepithelial neoplasia. Computed tomography (CT) showed no lymph node metastasis. Three days later, the endoscopist successfully removed the tumor completely using endoscopic submucosal dissection (ESD) without any residue (Figure 3).</p>
<p>The histopathological diagnosis was a well-differentiated adenocarcinoma, the depth of invasion was limited to the mucosal layer, and the lesion range was 1.1cm ×1.0cm (Figure 4). Both the lateral and base margins were negative, and the surrounding gastric mucosa showed severe atrophic gastritis and intestinal metaplasia. Immunohistochemical findings were as follows: MUC5AC (+), MUC6 (+), CD10 (+), MSH2 (+), MSH6 (+) and MUC2 (-). The patient was discharged from the hospital without any postoperative complications. At the last few follow-ups, the patient recovered well, was in stable condition without any discomfort, and did not require any adjuvant systemic chemotherapy.</p>
<p><img src="https://file.luminescience.cn/660cc3d53476b1712112597" alt="" width="500" height="333" /></p>
<p><strong style="font-size: 14;">Figure 1.</strong> ENDOANGEL red frame suggests lesions. The lesions were slightly uplifted and the surface was slightly yellow and granular.</p>
<p><img src="https://file.luminescience.cn/660cc4230e4b51712112675" alt="" width="500" height="340" /></p>
<p><strong style="font-size: 14;">Figure 2. </strong>The lesions were observed under narrow-band imaging (ME-NBI). There was a clear demarcation line (DL) around the lesion with irregular glands, and microvessels varied in diameter and size and were irregular in shape. And ENDOANGEL diagnosed the lesion as differentiated carcinoma.</p>
<p><img src="https://file.luminescience.cn/660cc525dbe4e1712112933" alt="" width="500" height="376" /></p>
<p><strong style="font-size: 14;">Figure 3.</strong> The lesions were resected by ESD. The assessment of lesion boundary before ESD(A) and the correspondence between tissue specimens resected by ESD(B) and mucosal lesions under white light endoscopy(C). O: oral-lateral, A: anal-lateral.</p>
<p><img src="https://file.luminescience.cn/660cc593a52e61712113043" alt="" width="500" height="240" /></p>
<p><strong style="font-size: 14;">Figure 4. </strong>Histopathological findings. The infiltration depth of adenocarcinoma cells did not penetrate the mucosal layer.</p>
<h5 class="journalThemeColor">Discussion and conclusions</h5>
<p>The mucosal changes in early gastric cancer are subtle and easily confused with benign gastric mucosal lesions such as gastritis, which endoscopists tend to ignore. Once gastric early cancer is missed, it will have a serious impact on the patient's health. We report a case of early gastric cancer detected by an AI-assisted endoscopist in a real clinical setting. Under white light endoscopy, the lesion showed slight eminence and yellowish and granular appearance on the surface, which differed greatly from typical early cancer features and was easy to miss.</p>
<p>In recent years, artificial intelligence has shown great potential in gastrointestinal endoscopy, which is expected to improve the current problem of the low detection rate of gastric early cancer. Hirasawa et al. were the first to report a deep learning model that could automatically detect gastric cancer in still images, with an overall sensitivity of 92.2% and a positive predictive value of 30.6% <a href="#reference_list_7">[8]</a>. Tang et al. developed an early gastric cancer detection system with an accuracy of 85.1% to 91.2% in a retrospective analysis <a href="#reference_list_8">[9]</a>. When assisted by the AI system, the performance of trainee endoscopists for EGC was comparable to that of expert endoscopists. Wu et al. developed an artificial intelligence system called ENDOANGEL <a href="#reference_list_3">[4]</a>, which could detect early gastric cancer with an accuracy of 92.5% under white light endoscopy. Subsequently, they pretested the performance of the AI system in detecting EGC in a multicenter randomized controlled trial <a href="#reference_list_9">[10]</a>. Furthermore, they evaluated the effect of ENDOANGEL in clinical practice in a randomized controlled clinical trial <a href="#reference_list_10">[11]</a>. The results showed that the use of the AI system could reduce the gastric neoplasm miss rate. Subsequently, ENDOANGEL achieved a sensitivity of 91.8% and a specificity of 92.4% on diagnosing neoplasms in another prospective clinical trial <a href="#reference_list_11">[12]</a>. These studies suggest that AI has great potential for diagnosing ECG.</p>
<p>Magnifying narrow-band imaging improves the detection of EGC, and experienced endoscopists can successfully make a preliminary diagnosis of the nature of differentiation and depth of infiltration of lesions. However, WLE is still the main way to screen for EGC. Studies have shown that the sensitivity for diagnosing EGC in WLE is only 48% <a href="#reference_list_12">[13]</a>, depending on the skill and experience level of endoscopists. Wu et al. held a nationwide human-machine competition to compare the performance of an AI system and endoscopists using 100 EGD videos<a href="#reference_list_13">[14]</a>. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, while those of endoscopists were only 83.51% and 87.13%. These results indicate that AI systems have great potential to improve the detection rate of EGC, especially for inexperienced primary endoscopists or endoscopists in primary hospitals in remote areas. Similarly, for experienced endoscopists, an AI system can act as an additional reminder to reduce the missed diagnosis rate of EGC.</p>
<p>The gastric early cancer reported in this paper is an example of the application of artificial intelligence in a real clinical environment. In the future, we expect greater breakthroughs in gastrointestinal endoscopy by artificial intelligence.</p>
<h5 class="journalThemeColor">Ethics and consent</h5>
<p>Informed consent was obtained from the patient for the publication of this report.</p>
<h5 class="journalThemeColor">Acknowledgment</h5>
<p>We would like to acknowledge with gratitude the contribution of the colleagues from the Department of Gastroenterology, Wenzhou Central Hospital.</p>
<h5 class="journalThemeColor">Authors' contributions</h5>
<p>Lin J. designed the study and did the literature research. Tao X. wrote the first draft of the report, which was further edited by all authors. All authors read and approved the final manuscript.</p>
<h5 class="journalThemeColor">Disclosure</h5>
<p>There are no financial conflicts of interest to disclose.</p>

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: A Cancer Journal for Clinicians. 2021 May;71(3):209-249. doi:10.3322/caac.21660

[2]Katai H, Ishikawa T, Akazawa K, et al. Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001-2007). Gastric Cancer. 2018; 21:144-154. doi: 10.1007/ s10120-017-0716-7.

[3]Rutter MD, Senore C, Bisschops R, et al. The European Society of Gastrointestinal endoscopy quality improvement initiative: developing performance measures. Endoscopy. 2016; 48:81-9. doi: 10.1055/s-0035-1569580.

[4]Wu L, Zhang J, Zhou W, et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut. 2019 Dec;68(12):2161-2169. doi: 10.1136/gutjnl-2018-317366.

[5]Wu L, Zhou W, Wan X, et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy. 2019; 51: 522-531. doi: 10.1055/a-0855-3532.

[6]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 May; 53(5):469-477. doi: 10.1055/ a-1229-0920.

[7]Tang D, Zhou J, Wang L, et al. A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video). Frontiers in Oncology. 2021 Apr. 20;11:622827. doi: 10.3389/fonc.2021.622827.

[8]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. doi: 10.1007/s10120-018-0793-2.

[9]Tang D, Wang L, Ling T, et al. Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: a multicentre retrospective diagnostic study. EBioMedicine. 2020; 62: 103146. doi: 10.1016/j.ebiom.2020.103146.

[10]Wu L, He X, Liu M 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 Dec; 53(12):1199-1207. doi: 10.1055/a-1350-5583.

[11]Wu L, Shang R, Sharma P. et al. Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-center, tandem, randomized controlled trial. Lancet Gastroenterol Hepatol. 2021; 6(9):700-708. doi: 10.1016/S2468-1253(21)00216-8.

[12]Wu L, Xu M, Jiang X, et al. Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointestinal Endoscopy. 2021 Sep.20; S0016-5107(21)01648-5. doi: 10.1016/ j.gie.2021.09.017.

[13]Zhang Q, Wang F, Chen ZY, et al. Comparison of the diagnostic efficacy of white light endoscopy and magnifying endoscopy with narrow band imaging for early gastric cancer: a meta-analysis. Gastric Cancer. 2016 Apr; 19(2):543-552. doi: 10.1007/s10120-015-0500-5.

[14]Wu L, Wang J, He X, et al. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointestinal Endoscopy. 2022 Jan; 95(1):92-104.e3. doi:10.1016/j.gie.2021.06.033.

Download PDF

Download XML

Figures

The lesions were observed under narrow-band imaging (ME-NBI). There was a clear demarcation line (DL) around the lesion with irregular glands, and microvessels varied in diameter and size and were irregular in shape. And ENDOANGEL diagnosed the lesion as differentiated carcinoma.

How to Cite

Lin, J., X. Tao, and J. Pan. “An Artificial Intelligence-Based System Assisted Endoscopists to Detect Early Gastric Cancer: A Case Report”. Journal of Digital Health, vol. 1, no. 1, Mar. 2022, pp. 25-29, doi:10.55976/jdh.1202214525-29.

Download Citation

Issue

Journal of Digital Health, Volume 1 Issue 1, 2022

Section

Case Report

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright licenses detail the rights for publication, distribution, and use of research. Open Access articles published by Luminescience do not require transfer of copyright, as the copyright remains with the author. In opting for open access, the author(s) should agree to publish the article under the CC BY license (Creative Commons Attribution 4.0 International License). The CC BY license allows for maximum dissemination and re-use of open access materials and is preferred by many research funding bodies. Under this license, users are free to share (copy, distribute and transmit) and remix (adapt) the contribution, including for commercial purposes, providing they attribute the contribution in the manner specified by the author or licensor.

Home /

Archives /

Journal of Digital Health, Volume 1 Issue 1, 2022 /

Case Report

An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report

Abstract

How to Cite

Issue

Section

License

Follow Luminescience

Further Information

Home / Archives / Journal of Digital Health, Volume 1 Issue 1, 2022 / Case Report

An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report

Abstract

How to Cite

Issue

Section

License

Follow Luminescience

Further Information

Home /

Archives /

Journal of Digital Health, Volume 1 Issue 1, 2022 /

Case Report