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5/8/2022 7:16:29 AM | Browse: 123 | Download: 142

Publication Name	World Journal of Gastroenterology
Manuscript ID	72837
Country/Territory	South Korea

Category	Gastroenterology and Hepatology
Manuscript Type	Retrospective Cohort Study
Article Title	Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer
Manuscript Source	Unsolicited Manuscript
All Author List	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 and Jae J. Kim
Funding Agency and Grant Number
Corresponding Author	Hyuk Lee, MD, PhD, Doctor, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. leehyuk@skku.edu., Seoul 135710, South Korea. leehyuk@skku.edu
Key Words	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.
Citation	Na JE, Lee YC, Kim TJ, Lee H, Won HH, Min YW, Min BH, Lee JH, Rhee PL, Kim JJ. Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer. World J Gastroenterol 2022; 28(24): 2721-2732

Received

2021-10-30 10:38

Peer-Review Started

2021-10-30 10:38

To Make the First Decision

Return for Revision

2022-03-11 01:46

Revised

2022-03-25 13:20

Second Decision

2022-05-07 07:56

Accepted by Journal Editor-in-Chief

Accepted by Company Editor-in-Chief

2022-05-08 07:16

Articles in Press

2022-05-08 07:16

Publication Fee Transferred

Edit the Manuscript by Language Editor

2022-04-30 00:38

Typeset the Manuscript

2022-06-10 08:32

ISSN	1007-9327 (print) and 2219-2840 (online)
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/
Copyright	© The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com

Assistant Editor	Assistant Editor
Science Editor	Ya-Juan Ma (Quit 2023)
Reviewer Code	06201645, 05461735, and 03668600
Editorial Director
Journal Chief Editor
Company Chief Editor	Lian-Sheng Ma
Language Editor	Donna Fox
Production Editor	Yu-Xi Chen
Responsible Production Editor	Yu-Xi Chen
Responsible Science Editor
Proofing Editorial Director	Jin-Lei Wang
Proofing Editor-in-Chief	Lian-Sheng Ma