Yang YH, Li Y. Deep learning radiomic analysis in the prediction of MYCN status and survival outcome in children with neuroblastoma. World J Clin Oncol 2026; 17(3): 114744 [DOI: 10.5306/wjco.v17.i3.114744]
Corresponding Author of This Article
Yu-Han Yang, MD, West China Hospital, Sichuan Medical University, No. 17 People’s South Road, Chengdu 610041, Sichuan Province, China. yyh_1023@163.com
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Pediatrics
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Retrospective Cohort Study
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Mar 24, 2026 (publication date) through Mar 26, 2026
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World Journal of Clinical Oncology
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2218-4333
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Yang YH, Li Y. Deep learning radiomic analysis in the prediction of MYCN status and survival outcome in children with neuroblastoma. World J Clin Oncol 2026; 17(3): 114744 [DOI: 10.5306/wjco.v17.i3.114744]
World J Clin Oncol. Mar 24, 2026; 17(3): 114744 Published online Mar 24, 2026. doi: 10.5306/wjco.v17.i3.114744
Deep learning radiomic analysis in the prediction of MYCN status and survival outcome in children with neuroblastoma
Yu-Han Yang, Yuan Li
Yu-Han Yang, Yuan Li, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Yang YH and Li Y contributed to conceptualization, computed tomography image segmentation and regions of interest delineation, writing - original draft preparation; writing - review and editing; Yang YH contributed to study design and methodology, data collection and curation, model development, and statistical analysis. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.
Institutional review board statement: This study involving human participants was reviewed and approved by the Institutional Review Boards of the participating institutions. 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.
Corresponding author: Yu-Han Yang, MD, West China Hospital, Sichuan Medical University, No. 17 People’s South Road, Chengdu 610041, Sichuan Province, China. yyh_1023@163.com
Received: September 28, 2025 Revised: October 21, 2025 Accepted: January 28, 2026 Published online: March 24, 2026 Processing time: 177 Days and 19 Hours
Abstract
BACKGROUND
Neuroblastoma is the most common extracranial solid tumor in childhood, and its prognosis is strongly influenced by MYCN amplification. Non-invasive imaging biomarkers using deep learning (DL)-based radiomic analysis could capture whole-tumor characteristics to improve preoperative risk stratification and treatment planning.
AIM
To evaluate the performance of a DL-based strategy on contrast-enhanced computed tomography to predict the presence of MYCN amplification and clinical outcomes, event-free survival in patients with neuroblastoma.
METHODS
All 103 eligible patients were included retrospectively between 2008 and 2015, and assigned into the training cohort (n = 72) from one institution and the testing cohort (n = 31) from the other institution. We extracted DL-based features on pretrained convolutional neural networks via transfer learning automatically, which were classified by a support vector machine. A DL-based signature was formed from the DL-based model with the optimal area under the receiver operating characteristic curve (AUC) in the testing cohort. An integrated nomogram model, including significant clinical variables and the DL-based signature, was constructed for the prediction of histological patterns. Survival analysis of MYCN amplification identified by a nomogram predicted and histopathological results were evaluated in the prediction of tumor-associated events.
RESULTS
The nomogram model for MYCN amplification represented great prediction and discriminative performance with an AUC of 0.959 and accuracy of 96.7% in the training cohort and an AUC of 0.819 and accuracy of 74.7% in the testing cohort. Predicted probabilities ≤ -1.996 was considered as non-amplified MYCN status, and predicted probabilities > -1.996 was considered as amplified MYCN status. The non-amplified and amplified MYCN status showed a significant survival difference in event-free survival, categorized by both nomogram predicted and histopathological results.
CONCLUSION
The integrated clinical-DL model achieved accurate abilities in the identification of MYCN amplification before surgery, which might optimize therapeutic strategy and improve patients’ survival in neuroblastomas.
Core Tip: We constructed a deep learning (DL)-based radiomics signature on computed tomography, which had the ability to identify MYCN amplification in neuroblastoma. Integrating the DL-based radiomics signature and clinical predictors, the nomogram model showed improvement in the prediction of MYCN amplification in neuroblastomas. The DL-based radiomics signature was found to be associated with disease-specific events of neuroblastomas significantly after radical resection.
