| 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. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & 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 |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2022-06-24 13:12 |
| 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 |
| URL |
https://www.wjgnet.com/1007-9327/full/v28/i24/2721.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v28.i24.2721 |
