An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video)

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.

Article  PubMed  Google Scholar 

Vasconcelos AC, Dinis-Ribeiro M, Libânio D. Endoscopic resection of early gastric cancer and pre-malignant gastric lesions. Cancers (Basel). 2023;15:3084.

Article  PubMed  Google Scholar 

Ahn HS, Lee HJ, Yoo MW, Kim SG, Im JP, Kim SH, et al. Diagnostic accuracy of T and N stages with endoscopy, stomach protocol CT, and endoscopic ultrasonography in early gastric cancer. J Surg Oncol. 2009;99:20–7.

Article  PubMed  Google Scholar 

Wani AH, Parry AH, Feroz I, Choh NA. Preoperative staging of gastric cancer using computed tomography and its correlation with histopathology with emphasis on multi-planar reformations and virtual gastroscopy. J Gastrointest Cancer. 2021;52:606–15.

Article  PubMed  Google Scholar 

Kim TH, Kim IH, Kang SJ, Choi M, Kim BH, Eom BW, et al. Korean practice guidelines for gastric cancer 2022: An evidence-based, multidisciplinary approach. J Gastric Cancer. 2023;23:3–106.

Article  PubMed  PubMed Central  Google Scholar 

Association JGC. Japanese Gastric Cancer Treatment Guidelines 2021. Gastric Cancer. 2023;26:1–25.

Article  Google Scholar 

Shim CN, Kim H, Kim DW, Chung HS, Park JC, Lee H, et al. Clinicopathologic factors and outcomes of histologic discrepancy between differentiated and undifferentiated types after endoscopic resection of early gastric cancer. Surg Endosc. 2014;28:2097–105.

Article  PubMed  Google Scholar 

Aliaga Ramos J, Pedrosa MS, Yoshida N, Abdul Rani R, Arantes VN. Histopathologic diagnosis discrepancies between preoperative endoscopic forceps biopsies and specimens resected by endoscopic submucosal dissection in superficial gastric neoplasms. J Clin Gastroenterol. 2023;57:74–81.

Article  CAS  PubMed  Google Scholar 

Kim Y, Yoon HJ, Kim JH, Chun J, Youn YH, Park H, et al. Effect of histologic differences between biopsy and final resection on treatment outcomes in early gastric cancer. Surg Endosc. 2020;34:5046–54.

Article  PubMed  Google Scholar 

Choi J, Kim SG, Im JP, Kim JS, Jung HC, Song IS. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy. 2010;42:705–13.

Article  CAS  PubMed  Google Scholar 

Shi D, Xi XX. Factors affecting the accuracy of endoscopic ultrasonography in the diagnosis of early gastric cancer invasion depth: A meta-analysis. Gastroenterol Res Pract. 2019;2019:8241381.

Article  PubMed  PubMed Central  Google Scholar 

Takamaru H, Yoshinaga S, Takisawa H, Oda I, Katai H, Sekine S, et al. Endoscopic ultrasonography miniature probe performance for depth diagnosis of early gastric cancer with suspected submucosal invasion. Gut Liver. 2020;14:581–8.

Article  PubMed  Google Scholar 

Lee J, Lee H, Chung JW. The role of artificial intelligence in gastric cancer: Surgical and therapeutic perspectives: A comprehensive review. J Gastric Cancer. 2023;23:375–87.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chang YH, Shin CM, Lee HD, Park JB, Jeon J, Kang S, et al. Clinical evaluation of an artificial intelligence system for upper gastrointestinal endoscopy on detecting and diagnosing gastric lesions pathologically pre-diagnosed with atypia and gastric dysplasia: A pilot study. Gastrointest Endosc. 2023;97:764.

Article  Google Scholar 

Bang CS. Artificial intelligence in the analysis of upper gastrointestinal disorders. Korean J Helicobacter Up Gastrointest Res. 2021;21:300–10.

Article  Google Scholar 

Kim JH, Oh SI, Han SY, Keum JS, Kim KN, Chun JY, et al. An optimal artificial intelligence system for real-time endoscopic prediction of invasion depth in early gastric cancer. Cancers (Basel). 2022;14:6000.

Article  PubMed  Google Scholar 

Park CH, Yang DH, Kim JW, Kim JH, Kim JH, Min YW, et al. Clinical practice guideline for endoscopic resection of early gastrointestinal cancer. Korean J Helicobacter Up Gastrointest Res. 2020;20:117–45.

Article  Google Scholar 

Lee S, Kim SG, Cho SJ. ision to perform additional surgery after non-curative endoscopic submucosal dissection for gastric cancer based on the risk of lymph node metastasis: A long-term follow-up study. Surg Endosc. 2023;37:7738–48.

Article  PubMed  Google Scholar 

Goto A, Kubota N, Nishikawa J, Ogawa R, Hamabe K, Hashimoto S, et al. Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer. Gastric Cancer. 2023;26:116–22.

Article  PubMed  Google Scholar 

Tan M, Le Q 2019 EfficientNet Rethinking model scaling for convolutional neural networks. In Kamalika C, Ruslan S, (eds). Proceedings of the 36th international conference on machine learning Proceedings of the machine learning research, PMLR, London

Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T. Analyzing and improving the image quality of StyleGAN. IEEE Publications. 2019;2020:8107–16.

Google Scholar 

Ahn JY, Jung HY, Choi KD, Choi JY, Kim MY, Lee JH, et al. Endoscopic and oncologic outcomes after endoscopic resection for early gastric cancer: 1370 cases of absolute and extended indications. Gastrointest Endosc. 2011;74:485–93.

Article  PubMed  Google Scholar 

Bang CS, Park JM, Baik GH, Park JJ, Joo MK, Jang JY, et al. Therapeutic outcomes of endoscopic resection of early gastric cancer with undifferentiated-type histology: A Korean ESD registry database analysis. Clin Endosc. 2017;50:569–77.

Article  PubMed  PubMed Central  Google Scholar 

Lee JH, Kim JH, Rhee K, Huh CW, Lee YC, Yoon SO, et al. Undifferentiated early gastric cancer diagnosed as differentiated histology based on forceps biopsy. Pathol Res Pract. 2013;209:314–8.

Article  PubMed  Google Scholar 

Shibagaki K, Amano Y, Ishimura N, Taniguchi H, Fujita H, Adachi S, et al. Diagnostic accuracy of magnification endoscopy with acetic acid enhancement and narrow-band imaging in gastric mucosal neoplasms. Endoscopy. 2016;48(1):16–25.

PubMed  Google Scholar 

Muto M, Yao K, Kaise M, Kato M, Uedo N, Yagi K, et al. Magnifying endoscopy simple diagnostic algorithm for early gastric cancer (MESDA-G). Dig Endosc. 2016;28(4):379–93.

Article  PubMed  Google Scholar 

Ling T, Wu L, Fu Y, Xu Q, An P, Zhang J, 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;53:469–77.

Article  PubMed  Google Scholar 

Kim GH. Systematic endoscopic approach to early gastric cancer in clinical practice. Gut Liver. 2021;15(6):811–7.

Article  PubMed  PubMed Central  Google Scholar 

Choi J, Kim SG, Im JP, Kim JS, Jung HC, Song IS. Endoscopic prediction of tumor invasion depth in early gastric cancer. Gastrointest Endosc. 2011;73(5):917–27.

Article  PubMed  Google Scholar 

Tsujii Y, Kato M, Inoue T, Yoshii S, Nagai K, Fujinaga T, et al. Integrated diagnostic strategy for the invasion depth of early gastric cancer by conventional endoscopy and EUS. Gastrointest Endosc. 2015;82(3):452–9.

Article  PubMed  Google Scholar 

Abe S, Oda I, Shimazu T, Kinjo T, Tada K, Sakamoto T, et al. Depth-predicting score for differentiated early gastric cancer. Gastric Cancer. 2011;14(1):35–40.

Article  PubMed  Google Scholar 

Zhu Y, Wang QC, Xu MD, Zhang Z, Cheng J, Zhong YS, et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc. 2019;89:806-815.e1.

Article  PubMed  Google Scholar 

Hamada K, Kawahara Y, Tanimoto T, Ohto A, Toda A, Aida T, et al. Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images. J Gastroenterol Hepatol. 2022;37:352–7.

Article  CAS  PubMed  Google Scholar 

Nagao S, Tsuji Y, Sakaguchi Y, Takahashi Y, Minatsuki C, Niimi K, et al. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: Efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. Gastrointest Endosc. 2020;92:866-873.e1.

Article  PubMed  Google Scholar 

Yoon HJ, Kim S, Kim JH, Keum JS, Oh SI, Jo J, et al. A lesion-based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J Clin Med. 2019;8:1310.

Article  PubMed  PubMed Central 

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