Skip to main content

Advertisement

SpringerLink
Log in

Computer-aided demarcation of early gastric cancer: a pilot comparative study with endoscopists

  • Original Article—Alimentary Tract
  • Published:
Journal of Gastroenterology Aims and scope Submit manuscript

Abstract

Background

Precise area diagnosis of early gastric cancer (EGC) is critical for reliable endoscopic resection. Computer-aided diagnosis (CAD) shows strong potential for detecting EGC and reducing cancer-care disparities caused by differences in endoscopists’ skills. To be used in clinical practice, CAD should enable both the detection and the demarcation of lesions. This study proposes a scheme for the detection and delineation of EGC under white-light endoscopy and validates its performance using 1-year consecutive cases.

Methods

Only 300 endoscopic images randomly selected from 68 consecutive cases were used for training a convolutional neural network. All cases were treated with endoscopic submucosal dissection, enabling the accumulation of a training dataset in which the extent of lesions was precisely determined. For validation, 462 cancer images and 396 normal images from 137 consecutive cases were used. From the validation results, 38 randomly selected images were compared with those delineated by six endoscopists.

Results

Successful detections of EGC in 387 cancer images (83.8%) and the absence of lesions in 307 normal images (77.5%) were achieved. Positive and negative predictive values were 81.3% and 80.4%, respectively. Successful detection was achieved in 130 cases (94.9%). We achieved precise demarcation of EGC with a mean intersection over union of 66.5%, showing the extent of lesions with a smooth boundary; the results were comparable to those achieved by specialists.

Conclusions

Our scheme, validated using 1-year consecutive cases, shows potential for demarcating EGC. Its performance matched that of specialists; it might therefore be suitable for clinical use in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Sano T, Coit DG, Kim HH, et al. Proposal of a new stage grouping of gastric cancer for TNM classification: international gastric cancer association staging project. Gastric Cancer. 2017;20:217–25.

    Article  PubMed  Google Scholar 

  2. Katai H, Ishikawa T, Akazawa K, Registration Committee of the Japanese Gastric Cancer Association, 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;21:144–54.

    Article  PubMed  Google Scholar 

  3. Ono H, Kondo H, Gotoda T, et al. Endoscopic mucosal resection for treatment of early gastric cancer. Gut. 2001;48:225–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Necula L, Matei L, Dragu D, et al. Recent advances in gastric cancer early diagnosis. World J Gastroenterol. 2019;25:2029–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Menon S, Trudgill N. How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis. Endosc Int Open. 2014. https://doi.org/10.1055/s-0034-1365524.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hosokawa O, Hattori M, Douden K, et al. Difference in accuracy between gastroscopy and colonoscopy for detection of cancer. Hepatogastroenterology. 2007;54:442–4.

    PubMed  Google Scholar 

  7. Choi KS, Jun JK, Park EC, et al. Performance of different gastric cancer screening methods in Korea: a population-based study. PLoS ONE. 2012;7: e50041.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med. 2018;169:357–66.

    Article  PubMed  Google Scholar 

  9. Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology. 2018;155:1069-1078.e8.

    Article  PubMed  Google Scholar 

  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;21:653–60.

    Article  PubMed  Google Scholar 

  11. Min JK, Kwak MS, Cha JM. Overview of deep learning in gastrointestinal endoscopy. Gut Liver. 2019;13:388–93.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ebigbo A, Palm C, Probst A, et al. A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology. Endosc Int Open. 2019;7:E1616–23.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Choi J, Shin K, Jung J, et al. Convolutional neural network technology in endoscopic imaging: artificial intelligence for endoscopy. Clinical Endosc. 2020;53:117–26.

    Article  Google Scholar 

  14. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

    Article  CAS  PubMed  Google Scholar 

  15. Sakai Y, Takemoto S, Hori K, et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2018; 4138–4141

  16. Pimentel-Nunes P, Dinis-Ribeiro M, Ponchon T, et al. Endoscopic submucosal dissection: European society of gastrointestinal endoscopy (ESGE) guideline. Endoscopy. 2015;47:829–54.

    Article  PubMed  Google Scholar 

  17. Nagahama T, Yao K, Uedo N, et al. Delineation of the extent of early gastric cancer by magnifying narrow-band imaging and chromoendoscopy: a multicenter randomized controlled trial. Endoscopy. 2018;50:566–76.

    Article  PubMed  Google Scholar 

  18. Nagahama T, Yao K, Maki S, et al. Usefulness of magnifying endoscopy with narrow-band imaging for determining the horizontal extent of early gastric cancer when there is an unclear margin by chromoendoscopy (with video). Gastrointest Endosc. 2011;74:1259–67.

    Article  PubMed  Google Scholar 

  19. Nonaka K, Namoto M, Kitada H, et al. Usefulness of the DL in ME with NBI for determining the expanded area of early-stage differentiated gastric carcinoma. World J Gastrointest Endosc. 2012;4:362–7.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gotoda T, Yamamoto H, Soetikno RM. Endoscopic submucosal dissection of early gastric cancer. J Gastroenterol. 2006;41:929–42.

    Article  PubMed  Google Scholar 

  21. Isomoto H, Shikuwa S, Yamaguchi N, et al. Endoscopic submucosal dissection for early gastric cancer: a large-scale feasibility study. Gut. 2009;58:331–6.

    Article  CAS  PubMed  Google Scholar 

  22. Gotoda T, Jung HY. Endoscopic resection (endoscopic mucosal resection/ endoscopic submucosal dissection) for early gastric cancer. Dig Endosc. 2013;25:55–63.

    Article  PubMed  Google Scholar 

  23. Chollet F, Keras. GitHub repository. 2015. https://github.com/fchollet/keras

  24. Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data. 2016. https://doi.org/10.1186/s40537-016-0043-6.

    Article  Google Scholar 

  25. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR). 2015; https://doi.org/10.1109/CVPR.2015.7298594.

  26. Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52.

    Article  Google Scholar 

  27. Everingham M, Gool L, Williams CK, et al. The PASCAL visual object classes (VOC) challenge. Int J Comput Vis. 2010;88:303–38.

    Article  Google Scholar 

  28. Luo H, Xu G, Li C, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20:1645–54.

    Article  CAS  PubMed  Google Scholar 

  29. An P, Yang D, Wang J, et al. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer. 2020;23:884–92.

    Article  PubMed  Google Scholar 

  30. Jin P, Ji X, Kang W, et al. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol. 2020;146:2339–50.

    Article  PubMed  Google Scholar 

  31. Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. Lecture Notes in Computer Science. Springer, Cham. 2016; 9905:21–37.

  32. Hori K, Ikematsu H, Yamamoto Y, et al. Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system. Dig Endosc. 2022;34:1021–9.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP21K12742 and by the National Cancer Center research and development fund (29-A-10, 2020-A-10).

Funding

Japan Society for the Promotion of Science, JP21K12742, Satoko Takemoto, The National Cancer Center research and development fund, 29-A-10, Tomonori Yano, 2020-A-10, Tomonori Yano.

Author information

Authors and Affiliations

  1. Image Processing Research Team, Center for Advanced Photonics, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan

    Satoko Takemoto, Sakai Yoshimasa, Masaomi Nishimura & Hideo Yokota

  2. Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan

    Keisuke Hori, Keiichiro Nakajo, Atsushi Inaba, Maasa Sasabe, Naoki Aoyama, Takashi Watanabe, Nobuhisa Minakata, Hiroaki Ikematsu & Tomonori Yano

  3. Division of Science and Technology for Endoscopy, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan

    Keiichiro Nakajo, Hiroaki Ikematsu & Tomonori Yano

  4. Department of Internal Medicine, Tsuyama Chuo Hospital, Tsuyama, Japan

    Keisuke Hori

Authors
  1. Satoko Takemoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keisuke Hori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sakai Yoshimasa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masaomi Nishimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Keiichiro Nakajo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Atsushi Inaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maasa Sasabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Naoki Aoyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Takashi Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nobuhisa Minakata
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hiroaki Ikematsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hideo Yokota
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tomonori Yano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hideo Yokota.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1073 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Takemoto, S., Hori, K., Yoshimasa, S. et al. Computer-aided demarcation of early gastric cancer: a pilot comparative study with endoscopists. J Gastroenterol 58, 741–750 (2023). https://doi.org/10.1007/s00535-023-02001-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00535-023-02001-x

Keywords

Search

Navigation