Yang YH, Li Y. Magnetic resonance imaging-based deep-learning radiomics score for survival prediction and risk stratification in pediatric hepatoblastoma receiving surgical resection. World J Radiol 2026; 18(1): 115503 [DOI: 10.4329/wjr.v18.i1.115503]
Corresponding Author of This Article
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. l13258389785@126.com
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Pediatrics
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Retrospective Cohort Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Jan 28, 2026 (publication date) through Jan 28, 2026
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World Journal of Radiology
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1949-8470
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Yang YH, Li Y. Magnetic resonance imaging-based deep-learning radiomics score for survival prediction and risk stratification in pediatric hepatoblastoma receiving surgical resection. World J Radiol 2026; 18(1): 115503 [DOI: 10.4329/wjr.v18.i1.115503]
World J Radiol. Jan 28, 2026; 18(1): 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, Yuan Li
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. l13258389785@126.com
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
Abstract
BACKGROUND
Children with hepatoblastoma (HB) remain high heterogeneity with distinct survival outcomes among individuals after surgical resection. Therefore, it’s essential to identify high-risk patients with poor outcomes before surgery in order to add appropriate neoadjuvant chemotherapy for improving prognosis.
AIM
To evaluate the performance of a deep learning (DL)-based radiomics (DLBR) score at predicting event-free survival (EFS) in patients with HB at the early stage who underwent surgical resection.
METHODS
A total of 106 patients were included retrospectively at two hospitals who underwent magnetic resonance imaging scanning and surgical excision, and were assigned into the training cohort (n = 74) from one institution and the testing cohort (n = 32) from the other institution. The widely adopted clinicopathologic variables were collected, and the magnetic resonance imaging-derived DL-based features were extracted through automatic segmentation. We developed a DLBR score based on DL-based features and an integrated clinical-DL nomogram model, and validated them externally.
RESULTS
The DLBR score was generated incorporating four DL-based features, including three TI-derived features and one T2-derived feature. The integrated clinical-DL nomogram was constructed based on the Pretreatment Extension of Disease stage, alpha-fetoprotein concentration, and the DLBR score. The integrated nomogram had relatively better prognostic and calibration abilities and less opportunity for prediction error compared with the clinicopathologic predictors alone and the DLBR score alone in both training and external validation. Additionally, the DLBR score could stratify the HB patients into two EFS-related risk subgroups accurately, and showed fine distinction abilities to identify patients with different survival outcomes within identical subgroups of clinical predictors.
CONCLUSION
The DLBR score acted as a noninvasive and reliable tool for predicting EFS in early-stage HB patients receiving survival resection, and might instruct therapeutic plans for improving prognosis.
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.
