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Articles Published Processes
8/20/2025 8:41:24 AM | Browse: 49 | Download: 132
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Received |
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2025-03-21 16:57 |
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Peer-Review Started |
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2025-03-21 16:57 |
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To Make the First Decision |
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Return for Revision |
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2025-03-27 10:25 |
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Revised |
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2025-05-11 20:42 |
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Second Decision |
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2025-07-03 02:44 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-07-03 07:07 |
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Articles in Press |
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2025-07-03 07:07 |
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Publication Fee Transferred |
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2025-05-15 16:55 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-08-05 08:39 |
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Publish the Manuscript Online |
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2025-08-20 08:41 |
| ISSN |
2218-4333 (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) 2025. 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 |
Oncology |
| Manuscript Type |
Basic Study |
| Article Title |
Prognostic role of Ki-67 in colorectal carcinoma: Development and evaluation of machine learning prediction models
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Da-Tong Zeng, Ming-Jie Li, Rui Lin, Wei-Jian Huang, Shi-De Li, Wan-Ying Huang, Bin Li, Qi Li, Gang Chen and Jia-Shu Jiang |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project |
No. Z20210442 |
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| Corresponding Author |
Jia-Shu Jiang, Associate Chief Physician, Associate Professor, Researcher, Department of International Cooperation and External Exchange, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang Autonomous Region, China. jiangjiashu115@163.com |
| Key Words |
Machine learning; Colorectal carcinoma; Ki-67; Prognosis; Prediction |
| Core Tip |
This study pioneers the application of machine learning to predict Ki-67 status in colorectal carcinoma directly from hematoxylin and eosin-stained images. By analyzing data, 50% was identified as the optimal Ki-67 cutoff, with high-expression being linked to improved survival rates and low-expression being associated with advanced tumor stage and lymph node metastasis. Predictive models were developed using the support vector machine, random forest, and eXtreme gradient boosting algorithms, achieving area under the curve values (0.851-0.948 in training and 0.750-0.795 in the external validation group). This innovative approach highlights the potential of machine learning to enhance prognostic accuracy. |
| Publish Date |
2025-08-20 08:41 |
| Citation |
<p>Zeng DT, Li MJ, Lin R, Huang WJ, Li SD, Huang WY, Li B, Li Q, Chen G, Jiang JS. Prognostic role of Ki-67 in colorectal carcinoma: Development and evaluation of machine learning prediction models. <i>World J Clin Oncol</i> 2025; 16(8): 107306</p> |
| URL |
https://www.wjgnet.com/2218-4333/full/v16/i8/107306.htm |
| DOI |
https://dx.doi.org/10.5306/wjco.v16.i8.107306 |
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