BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
5/15/2025 10:28:50 AM | Browse: 22 | Download: 66
 |
Received |
|
2024-12-27 08:29 |
|
Peer-Review Started |
|
2024-12-27 08:29 |
 |
To Make the First Decision |
|
|
|
Return for Revision |
|
2025-01-13 02:01 |
|
Revised |
|
2025-01-20 14:16 |
|
Second Decision |
|
2025-02-26 02:39 |
|
Accepted by Journal Editor-in-Chief |
|
|
|
Accepted by Executive Editor-in-Chief |
|
2025-02-26 06:47 |
|
Articles in Press |
|
2025-02-26 06:47 |
|
Publication Fee Transferred |
|
2025-01-22 04:39 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2025-04-27 09:15 |
|
Publish the Manuscript Online |
|
2025-05-15 10:28 |
| 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) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| 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 Study |
| Article Title |
Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Zi-Wei Zhu, Jun Wu, Yang Guo, Qiong-Yuan Ren, Dong-Ning Li, Ze-Yu Li and Lei Han |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Joint Science and Technology Plan Project of Liaoning Province, China |
2024JH2/102600291 |
|
| Corresponding Author |
Lei Han, Associate Chief Physician, MD, Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang 110016, Liaoning Province, China. hanlei1974@sina.com |
| Key Words |
Hepatocellular carcinoma; Ki-67; Computed tomography; Machine learning; Radiomics |
| Core Tip |
Ki-67 expression is significantly correlated with hepatocellular carcinoma prognosis, and preoperative prediction of Ki-67 expression is crucial. To date, scholars have used radiomic features of tumour regions to predict their expression but have overlooked the important role of the peritumoral region. The findings in this study indicate that machine learning models that fully utilize the features of radiomics, tumour surrounding areas, and clinical factors can more accurately predict Ki-67 expression in hepatocellular carcinoma, thereby helping to improve personalized treatment strategies for liver cancer patients. |
| Publish Date |
2025-05-15 10:28 |
| Citation |
<p>Zhu ZW, Wu J, Guo Y, Ren QY, Li DN, Li ZY, Han L. Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features. <i>World J Gastrointest Oncol</i> 2025; 17(5): 104172</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i5/104172.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i5.104172 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
