| 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 |
Retrospective Cohort Study |
| Article Title |
Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yong-Hai Li, Gui-Xiang Qian, Ling Yao, Xue-Di Lei, Yu Zhu, Lei Tang, Zi-Ling Xu, Xiang-Yi Bu, Ming-Tong Wei, Jian-Lin Lu and Wei-Dong Jia |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Anhui Provincial Key Research and Development Plan |
No. 202104j07020048 |
|
| Corresponding Author |
Wei-Dong Jia, MD, PhD, Professor, Cheeloo College of Medicine, Shandong University, No. 44 Wen Hua Xi Road, Shandong 250021, China. jwd1968@ustc.edu.cn |
| Key Words |
Hepatocellular carcinoma; Ablation; Early recurrence; Radiomics; Deep learning; Peritumoral |
| Core Tip |
This study developed a predictive model for early recurrence (ER) in hepatocellular carcinoma (HCC) patients postablation by combining radiomics and deep learning. The model, which integrates intratumoral and peritumoral regions, demonstrated strong predictive performance, with area under the receiver operating characteristic curve of 0.924, 0.899, and 0.839 in the training, internal, and external validation sets, respectively. It offers a noninvasive and reliable method for ER prediction, providing valuable insights for treatment planning and prognosis in HCC patients. |
| Publish Date |
2025-06-13 13:39 |
| Citation |
<p>Li YH, Qian GX, Yao L, Lei XD, Zhu Y, Tang L, Xu ZL, Bu XY, Wei MT, Lu JL, Jia WD. Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study. <i>World J Gastrointest Oncol</i> 2025; 17(6): 106608</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i6/106608.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i6.106608 |
