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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Jan 28, 2026; 18(1): 115503
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.115503
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.115503
Magnetic resonance imaging-based deep-learning radiomics score for survival prediction and risk stratification in pediatric hepatoblastoma receiving surgical resection
Yu-Han Yang, West China Hospital, Sichuan University, Chengdu 6100041, Sichuan Province, China
Yuan Li, Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 6100000, Sichuan Province, China
Author contributions: Yang YH and Li Y contributed to study conception and design; Yang YH contributed to data acquisition, analysis, and data interpretation, drafting of the manuscript; Li Y contributed to critical revision.
Institutional review board statement: This retrospective study involving human participants was reviewed and approved by the Institutional Review Boards of the West China Hospital, Sichuan University and the First Hospital of Liangshan. All procedures were conducted in accordance with the ethical standards of the institutional and/or national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent statement: The requirement for written informed consent was waived by the institutional review boards of both participating institutions because the study was retrospective, used existing clinical and imaging records, and analyzed de-identified data.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: De-identified individual participant data that underlie the results reported in this article are available from the corresponding author upon reasonable request. Data sharing is subject to approval by the relevant institutional review boards and execution of a data-use agreement to ensure protection of patient privacy and compliance with applicable regulations. Due to institutional policies and patient privacy considerations, raw imaging data or any data containing potentially identifying information will not be publicly released.
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/
Corresponding author: Yuan Li, Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 6100000, Sichuan Province, China.
Received: October 20, 2025
Revised: November 12, 2025
Accepted: December 15, 2025
Published online: January 28, 2026
Processing time: 100 Days and 14.1 Hours
Revised: November 12, 2025
Accepted: December 15, 2025
Published online: January 28, 2026
Processing time: 100 Days and 14.1 Hours
Core Tip
Core Tip: In the present research, we generated a deep learning (DL) based radiomics score in the prediction of event-free survival for children with hepatoblastoma receiving surgical resection from multiple institutions, and developed an integrated clinical-DL nomogram based on widely accepted clinicopathologic predictors and the DL based radiomics score. The integrated nomogram showed great prediction performance for event-free survival with external validation, which might instruct therapeutic interventions. Further research is needed to validate the risk identification performance for improving clinical practicability and generality.