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Articles Published Processes
6/24/2022 1:12:05 PM | Browse: 407 | Download: 1446
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Received |
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2021-10-30 10:38 |
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Peer-Review Started |
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2021-10-30 10:38 |
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First Decision by Editorial Office Director |
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2022-03-11 01:46 |
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Return for Revision |
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2022-03-11 01:46 |
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Revised |
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2022-03-25 13:20 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2022-05-07 07:56 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2022-05-08 07:16 |
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Articles in Press |
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2022-05-08 07:16 |
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Edit the Manuscript by Language Editor |
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2022-04-30 00:38 |
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Typeset the Manuscript |
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2022-06-10 08:32 |
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Publish the Manuscript Online |
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2022-06-24 13:12 |
| 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
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| 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, 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
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| URL |
https://www.wjgnet.com/1007-9327/full/v28/i24/2721.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v28.i24.2721 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
