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Articles Published Processes
4/8/2026 7:28:53 AM | Browse: 37 | Download: 131
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Received |
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2025-10-22 09:47 |
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Peer-Review Started |
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2025-10-22 09:49 |
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First Decision by Editorial Office Director |
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2025-11-19 10:44 |
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Return for Revision |
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2025-11-20 13:54 |
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Revised |
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2025-12-04 12:32 |
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Publication Fee Transferred |
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2025-12-09 10:27 |
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Second Decision by Editor |
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2026-02-04 02:52 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-02-04 06:59 |
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Articles in Press |
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2026-02-04 06:59 |
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Edit the Manuscript by Language Editor |
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2026-02-10 10:45 |
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Typeset the Manuscript |
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2026-03-27 00:54 |
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Publish the Manuscript Online |
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2026-04-08 07:28 |
| ISSN |
1948-5204 (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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2026. 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 |
Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xue-Di Lei, Gui-Xiang Qian, Zhi-Gang Sun, Zi-Qi Tang, Yuan-Cheng Liu, Rui Du and Yong-Hai Li |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Anhui Provincial Research Project on the Inheritance and Innovation of Traditional Chinese Medicine |
No. 2024CCCX007 |
| Graduate Research and Innovation Program of Bengbu Medical University |
No. Byycxz24046 |
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| Corresponding Author |
Yong-Hai Li, MD, Department of Anorectal Surgery, The First People’s Hospital of Hefei, No. 390 Huaihe Road, Hefei 230001, Anhui Province, China. liyonghai@ahmu.edu.cn |
| Key Words |
Colorectal cancer; Radiomics; Deep learning; Lymph node metastasis; Prognosis; Contrast-enhanced computed tomography; Nomogram; Shapley additive explanation |
| Core Tip |
Accurate preoperative prediction of lymph node metastasis is crucial for optimizing treatment strategies in colorectal cancer. In this study, we developed an interpretable clinical-deep learning-radiomics nomogram (DLRN) by integrating clinical features with multi-regional radiomics and deep learning features. Moreover, the DLRN-based prognostic model effectively predicted 3-year recurrence-free survival. As a noninvasive preoperative tool, the DLRN demonstrated strong predictive accuracy for lymph node metastasis in colorectal cancer and offers a practical means for individualized risk stratification and informed treatment decision-making. |
| Publish Date |
2026-04-08 07:28 |
| Citation |
Lei XD, Qian GX, Sun ZG, Tang ZQ, Liu YC, Du R, Li YH. Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer. World J Gastrointest Oncol 2026; 18(4): 115635 |
| URL |
https://www.wjgnet.com/1948-5204/full/v18/i4/115635.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v18.i4.115635 |
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