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Articles Published Processes
12/26/2025 6:25:50 AM | Browse: 2 | Download: 1
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Received |
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2025-08-11 07:09 |
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Peer-Review Started |
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2025-08-11 07:09 |
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First Decision by Editorial Office Director |
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2025-08-25 07:36 |
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Return for Revision |
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2025-08-25 07:36 |
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Revised |
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2025-09-15 14:10 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-12-02 02:46 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-12-03 07:56 |
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Articles in Press |
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2025-12-03 07:56 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-12-23 00:30 |
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Publish the Manuscript Online |
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2025-12-26 06:25 |
| ISSN |
1949-8470 (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 |
Imaging Science & Photographic Technology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yue Shi, Peng Zhang, Li Li, Hui-Min Yang, Zu-Mao Li, Jing Zheng and Lin Yang |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Lin Yang, Department of Radiology, Interventional Medical Center, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, No. 63 Wenhua Road, Nanchong 637000, Sichuan Province, China. linyangmd@163.com |
| Key Words |
Machine learning; SHapley Additive exPlanations algorithms; Radiomic model; Hepatocellular carcinoma; Magnetic resonance imaging; Pathological grading; Inflammatory markers |
| Core Tip |
Despite the promising prospects of using artificial intelligence and machine learning for disease classification and prediction purposes, the complexity and lack of explainability of these methods make it difficult to apply the constructed models in clinical practice. This study aimed to develop and validate an interpretable machine learning model for conducting preoperative pathological grade prediction in hepatocellular carcinoma patients via a combination of multisequence magnetic resonance imaging radiomics and clinical features, which will help clinicians better understand the situation and develop personalized treatment plans. |
| Publish Date |
2025-12-26 06:25 |
| Citation |
Shi Y, Zhang P, Li L, Yang HM, Li ZM, Zheng J, Yang L. Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas. World J Radiol 2025; 17(12): 112911 |
| URL |
https://www.wjgnet.com/1949-8470/full/v17/i12/112911.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v17.i12.112911 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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