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Articles Published Processes
7/14/2025 12:55:51 PM | Browse: 5 | Download: 21
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Received |
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2025-03-03 03:19 |
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Peer-Review Started |
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2025-03-03 03:20 |
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To Make the First Decision |
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Return for Revision |
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2025-04-16 10:19 |
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Revised |
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2025-04-18 15:34 |
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Second Decision |
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2025-05-23 02:42 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-05-23 08:45 |
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Articles in Press |
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2025-05-23 08:45 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-06-30 01:58 |
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Publish the Manuscript Online |
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2025-07-14 12:55 |
| 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: https://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 |
Gastroenterology & Hepatology |
| Manuscript Type |
Editorial |
| Article Title |
Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Na Feng, Kun Wang and Yan Jiao |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Yan Jiao, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. lagelangri1@126.com |
| Key Words |
Hepatocellular carcinoma; Radiomics; Machine learning; Prognosis; Diagnosis |
| Core Tip |
The integration of radiomics with machine learning (ML) algorithms holds significant promise in improving the diagnosis and prognosis of hepatocellular carcinoma. Key radiomic features, such as texture, shape, and intensity, when combined with advanced ML techniques, can enhance tumor characterization, predict treatment responses, and provide better prognostic insights. However, challenges related to data heterogeneity, model interpretability, and multi-modal data integration must be addressed for these technologies to be widely adopted in clinical practice. |
| Publish Date |
2025-07-14 12:55 |
| Citation |
<p>Feng N, Wang K, Jiao Y. Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma. <i>World J Gastrointest Oncol</i> 2025; 17(7): 106610</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i7/106610.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i7.106610 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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