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Articles Published Processes
4/24/2026 2:48:51 AM | Browse: 1 | Download: 1
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Received |
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2025-12-28 08:35 |
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Peer-Review Started |
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2025-12-28 08:35 |
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First Decision by Editorial Office Director |
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2026-01-12 09:11 |
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Return for Revision |
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2026-01-12 09:11 |
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Revised |
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2026-01-21 03:34 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-02-04 02:44 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-02-04 12:34 |
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Articles in Press |
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2026-02-04 12:34 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-04-15 06:19 |
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Publish the Manuscript Online |
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2026-04-24 02:48 |
| 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) 2026. 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 |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Correspondence |
| Article Title |
Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Ujjayita Chowdhury, Atharva A Mahajan, Muthu Subash Kavitha, Ramya Lakshmi Rajendran, Prakash Gangadaran and Byeong-Cheol Ahn |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Byeong-Cheol Ahn, Department of Nuclear Medicine, School of Medicine, Kyungpook National University, 680, Gukchaebosang ro, Jung gu, Daegu 41944, South Korea. abc2000@knu.ac.kr |
| Key Words |
Pediatric hepatoblastoma; Magnetic resonance imaging radiomics; Deep learning; Event-free survival; Preoperative risk stratification; Alpha-fetoprotein; Pretreatment extension of disease stage; Nomogram |
| Core Tip |
Accurate preoperative risk stratification remains challenging in pediatric hepatoblastoma. This study demonstrates that a magnetic resonance imaging-based deep learning radiomics score predicts event-free survival and refines risk stratification beyond conventional clinical factors. Integration with pretreatment extension of disease stage and alpha-fetoprotein improves prognostic accuracy, supporting noninvasive, imaging-driven decision-making for individualized. |
| Publish Date |
2026-04-24 02:48 |
| Citation |
Chowdhury U, Mahajan AA, Kavitha MS, Rajendran RL, Gangadaran P, Ahn BC. Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma. World J Radiol 2026; 18(4): 118196 |
| URL |
https://www.wjgnet.com/1949-8470/full/v18/i4/118196.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v18.i4.118196 |
© 1993-2026 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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