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Articles Published Processes
6/30/2024 7:39:32 AM | Browse: 115 | Download: 591
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Received |
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2024-01-27 11:36 |
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Peer-Review Started |
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2024-01-27 11:36 |
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First Decision by Editorial Office Director |
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2024-03-08 06:12 |
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Return for Revision |
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2024-03-08 06:12 |
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Revised |
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2024-03-16 09:12 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2024-04-25 02:55 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2024-04-25 06:41 |
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Articles in Press |
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2024-04-25 06:41 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2024-06-12 07:30 |
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Publish the Manuscript Online |
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2024-06-30 07:39 |
| 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 |
Surgery |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Kai-Feng Yang, Sheng-Jie Li, Jun Xu and Yong-Bin Zheng |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Yong-Bin Zheng, PhD, Doctor, Doctor, Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, No. 100 Zhangzhidong Road, Wuhan 430030, Hubei Province, China. yongbinzheng@whu.edu.cn |
| Key Words |
Colorectal cancer; Synchronous liver metastasis; Gray-level co-occurrence matrix; Machine learning algorithm; Prediction model |
| Core Tip |
Our predictive model for synchronous liver metastasis (SLM) in colorectal cancer (CRC) patients can screen reliable predictive variables based on clinical features. This is crucial for predicting SLM in CRC and improving patient prognosis. Imaging omics is a discipline that has developed in recent years. Based on advanced deep learning algorithms, extracting imaging features will have practical clinical value for constructing prediction models for SLM in CRC. This study combines imaging and deep learning to construct an early warning prediction model, to provide necessary auxiliary predictions for the occurrence of SLM and guide clinical decision-making. |
| Publish Date |
2024-06-30 07:39 |
| Citation |
Yang KF, Li SJ, Xu J, Zheng YB. Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer. World J Gastrointest Surg 2024; 16(6): 1571-1581 |
| URL |
https://www.wjgnet.com/1948-9366/full/v16/i6/1571.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v16.i6.1571 |
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