Publication Name	World Journal of Clinical Oncology
Manuscript ID	114744
Country	China

Category	Pediatrics
Manuscript Type	Retrospective Cohort Study
Article Title	Deep learning radiomic analysis in the prediction of MYCN status and survival outcome in children with neuroblastoma
Manuscript Source	Invited Manuscript
All Author List	Yu-Han Yang and Yuan Li
Funding Agency and Grant Number
Corresponding Author	Yu-Han Yang, MD, West China Hospital, Sichuan University, No.17 People's South Road, Chengdu 610041, Sichuan Province, China. yyh_1023@163.com
Key Words	Neuroblastoma; MYCN amplification; Deep learning; Radiomics; Computed tomography; Event-free survival
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.
Citation	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; In press

Received	2025-09-28 00:38
Peer-Review Started	2025-09-28 00:38
First Decision by Editorial Office Director	2025-10-17 07:37
Return for Revision	2025-10-17 07:37
Revised	2025-10-21 20:53
Publication Fee Transferred
Second Decision by Editor	2026-01-28 02:41
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-28 09:42
Articles in Press	2026-01-28 09:42
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2218-4333 (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.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com