Retrospective Cohort Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 28, 2022; 28(24): 2721-2732
Published online Jun 28, 2022. doi: 10.3748/wjg.v28.i24.2721
Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer
Ji Eun Na, Yeong Chan Lee, Tae Jun Kim, Hyuk Lee, Hong-Hee Won, Yang Won Min, Byung-Hoon Min, Jun Haeng Lee, Poong-Lyul Rhee, Jae J Kim
Ji Eun Na, Department of Internal Medicine, Inje University Haeundae Paik Hospital, Busan 48108, South Korea
Ji Eun Na, Tae Jun Kim, Hyuk Lee, Yang Won Min, Byung-Hoon Min, Jun Haeng Lee, Poong-Lyul Rhee, Jae J Kim, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
Yeong Chan Lee, Hong-Hee Won, Department of Digital Health, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul 06351, South Korea
Author contributions: Na JE, Lee YC and Kim TJ contributed equally to this work as co-first authors of this paper; Na JE, Lee YC, Kim TJ, and Lee H contributed to the study concept and design, acquisition, analysis, or interpretation of data, and writing and drafting of the manuscript; Kim TJ, Lee H, Won HH, Min YW, Min BH, Lee JH, Rhee PL, and Kim JJ contributed to the critical revision of the manuscript for important intellectual content; Lee YC contributed to the statistical analysis; All authors approved the final submission.
Institutional review board statement: The Institutional review board of the Samsung Medical Center, Korea, approved this study, and the requirement for obtaining informed consent was waived owing to the study's retrospective nature.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: Data available on request due to privacy. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hyuk Lee, MD, PhD, Doctor, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, South Korea. leehyuk@skku.edu
Received: October 30, 2021
Peer-review started: October 30, 2021
First decision: March 11, 2022
Revised: March 25, 2022
Accepted: May 8, 2022
Article in press: May 8, 2022
Published online: June 28, 2022
Processing time: 237 Days and 2.6 Hours
ARTICLE HIGHLIGHTS
Research background

With the increasing rate of diagnosis at early stages of gastric cancer, endoscopic submucosal dissection (ESD) is being actively applied as the minimally invasive treatment. Bleeding is one of the significant complications, with an incidence of 3.6%–6.9%. Because bleeding after ESD requires hospitalization and hemostatic interventions, there is a need to predict patients at a high risk of bleeding after ESD.

Research motivation

Currently, artificial intelligence systems are being applied in various fields of gastroenterology. Deep learning among artificial intelligence systems was automatically trained so that it could be generalized well. There has been no study on the efficacy of deep learning for predicting post-ESD bleeding (PEB), and no study has compared these systems with a clinical model.

Research objectives

This study aimed to develop and compare the performance of the deep learning and clinical model for predicting PEB in early gastric cancer (EGC) patients.

Research methods

Patients who underwent ESD for EGC between January 2010 and June 2020 at the Samsung Medical Center, Seoul, South Korea, were screened retrospectively. We built a deep learning model and a clinical model based on the development set, which comprised 80% of the overall cohort. Subsequently, we validated the deep learning and clinical models in the validation set, which comprised 20% of the overall cohort. The deep learning model and the clinical model for prediction of bleeding after ESD were evaluated using two methods. First, sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic area curve along with the area under the curve (AUC) were analyzed. The performance with AUC was compared using the bootstrap test. Second, the risk stratification of PEB based on the development set was applied to the validation set and compared with the actual bleeding rate. The authors selected cutoff to discriminate the risk categories as low-, intermediate-, and high-risk at a bleeding rate of < 5% and < 9% in the development set referred to in a previous report.

Research results

Of the 5629 patients, 325 experienced PEB. The AUC for predicting PEB was 0.71 (95% confidence interval: 0.63-0.78) in the deep learning model and 0.70 (95% confidence interval: 0.62-0.77) in the clinical model, without significant difference (P = 0.730). In the validated set, the deep learning model showed an actual bleeding rate of 2.2%, 3.9%, and 11.6% in low-, intermediate-, high-risk categories, respectively; the clinical model showed an actual bleeding rate of 4.0%, 8.8%, and 18.2% in low-, intermediate-, high-risk categories, respectively.

Research conclusions

In conclusion, we introduced a deep learning model to predict the risk of bleeding after ESD in patients with EGC. The model demonstrated its performance as comparable to the clinical model.

Research perspectives

Based on the risk-prediction model, physicians could carefully assess the bleeding risk and perform preventive hemostasis during the procedure. Suppose additional management like the shielding method for preventing PEB in the selected high-risk group is attempted; in that case, it is anticipated that the deep learning model could support risk stratification.