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Articles Published Processes
5/30/2025 10:57:29 AM | Browse: 18 | Download: 48
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Received |
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2025-02-19 12:29 |
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Peer-Review Started |
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2025-02-19 12:29 |
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To Make the First Decision |
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Return for Revision |
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2025-03-28 09:30 |
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Revised |
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2025-04-05 13:18 |
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Second Decision |
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2025-05-12 02:38 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-05-12 08:35 |
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Articles in Press |
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2025-05-12 08:35 |
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Publication Fee Transferred |
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2025-04-08 12:36 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-05-24 04:34 |
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Publish the Manuscript Online |
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2025-05-30 10:57 |
| ISSN |
1948-9366 (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) 2024. 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 |
Emergency Medicine |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Da-Lue Li, Ling Zhu, Shun-Li Liu, Zhi-Bo Wang, Jing-Nong Liu, Xiao-Ming Zhou, Ji-Lin Hu and Rui-Qing Liu |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
No. 82000482 |
| China Postdoctoral Science Foundation funded |
No. 2023M741858 |
| China Crohn’s and Colitis Foundation |
No. CCCF-QF-2023C18-3 |
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| Corresponding Author |
Rui-Qing Liu, MD, Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16 Shinan Jiangsu Road, Qingdao 266000, Shandong Province, China. liuruiqing@qdu.edu.cn |
| Key Words |
Incarcerated inguinal hernia; Radiomics; Bowel resection; Unenhanced computed tomography; Texture analysis; Machine learning |
| Core Tip |
This study developed an innovative radiomic-clinical nomogram to predict bowel resection risks in patients with incarcerated inguinal hernia (IIH). By extracting 13 radiomic features from unenhanced computed tomography scans and combining them with clinical data, a predictive model was created. The nomogram showed strong performance with area under the curves of 0.864 in the training set and 0.800 in the test set. Decision curve analysis demonstrated that the integrated model outperformed standalone clinical and radiomic approaches, offering a valuable tool for improving clinical decision-making in IIH patient management. |
| Publish Date |
2025-05-30 10:57 |
| Citation |
<p>Li DL, Zhu L, Liu SL, Wang ZB, Liu JN, Zhou XM, Hu JL, Liu RQ. Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia. <i>World J Gastrointest Surg</i> 2025; 17(6): 106155</p> |
| URL |
https://www.wjgnet.com/1948-9366/full/v17/i6/106155.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v17.i6.106155 |
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