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
Abstract
BACKGROUND

Bleeding is one of the major complications after endoscopic submucosal dissection (ESD) in early gastric cancer (EGC) patients. There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.

AIM

To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.

METHODS

Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled, and post-ESD bleeding (PEB) was investigated retrospectively. We split the entire cohort into a development set (80%) and a validation set (20%). The deep learning and clinical model were built on the development set and tested in the validation set. The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.

RESULTS

A total of 5629 patients were included, and PEB occurred in 325 patients. The area under the curve 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). The patients expected to the low- (< 5%), intermediate- (≥ 5%, < 9%), and high-risk (≥ 9%) categories were observed with actual bleeding rate of 2.2%, 3.9%, and 11.6%, respectively, in the deep learning model; 4.0%, 8.8%, and 18.2%, respectively, in the clinical model.

CONCLUSION

A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.

Keywords: Clinical model; Deep learning model; Post-endoscopic submucosal dissection bleeding; Stratification of bleeding risk

Core Tip: Bleeding is one of the major complications after endoscopic submucosal dissection (ESD) in early gastric cancer patients and requires hospital-based intervention. We established a deep learning model to stratify the bleeding risk after ESD and demonstrated its performance compared with a clinical model. The deep learning model showed acceptable area under the curve and could stratify the post-ESD bleeding risk as low-, intermediate-, and high-risk categories, which correlated with actual bleeding rate comparatively. A deep learning model would be valuable in assessing the bleeding risk after ESD in early gastric cancer patients.