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Articles Published Processes
9/5/2025 6:36:16 AM | Browse: 512 | Download: 459
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Received |
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2025-07-04 01:54 |
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Peer-Review Started |
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2025-07-04 01:54 |
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First Decision by Editorial Office Director |
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2025-07-18 07:54 |
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Return for Revision |
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2025-07-18 07:54 |
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Revised |
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2025-08-01 02:00 |
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Publication Fee Transferred |
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2025-08-02 01:59 |
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Second Decision by Editor |
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2025-08-14 03:14 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-08-14 08:55 |
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Articles in Press |
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2025-08-14 08:55 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-09-02 05:53 |
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Publish the Manuscript Online |
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2025-09-05 06:36 |
| ISSN |
1007-9327 (print) and 2219-2840 (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 |
Oncology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Zi-Zheng Wang, Shao-Ming Song, Gong Zhang, Rui-Qiu Chen, Zhuo-Chao Zhang and Rong Liu |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| AI+ Health Collaborative Innovation Cultivation Project of Beijing City |
Z221100003522005 |
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| Corresponding Author |
Rong Liu, Faculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General Hospital, Fuxing Road No. 28, Haidian District, Beijing 100853, China. liurong301@126.com |
| Key Words |
Super-resolution reconstruction; Magnetic resonance imaging; Hepatocellular carcinoma; Histopathologic grade; Radiomics |
| Core Tip |
In this study, multiparametric magnetic resonance imaging radiomics could non-invasively classify histopathologic grade in hepatocellular carcinoma. The image quality is crucial for radiomics feature extraction and model development. Deep learning-based super-resolution (SR) reconstruction further improved the prediction by optimizing radiomics features. Deep learning-based SR reconstruction may provide deeper insights for precision medicine and disease management. |
| Publish Date |
2025-09-05 06:36 |
| Citation |
Wang ZZ, Song SM, Zhang G, Chen RQ, Zhang ZC, Liu R. Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma. World J Gastroenterol 2025; 31(34): 111541 |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i34/111541.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i34.111541 |
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