Deep learning radiomic analysis in the prediction of MYCN status and survival outcome in children with neuroblastoma

doi:10.5306/wjco.v17.i3.114744

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 17, Issue 3

This Article

(31)

(40)

(0)

(633)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Clinical Oncology

ISSN

2218-4333

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Cohort Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Clin Oncol. Mar 24, 2026; 17(3): 114744
Published online Mar 24, 2026. doi: 10.5306/wjco.v17.i3.114744

Table 1 The demographic and clinical characteristics between training and testing cohorts, n (%)

Characteristic	All subjects (n = 103)	Training cohort (n = 72)	Testing cohort (n = 31)	P value
Age, median (IQR), months	24 (17)	24 (17)	25 (18)	0.701
Gender				0.905
Male	81 (78.6)	57 (79.2)	24 (77.4)
Female	22 (21.4)	15 (20.8)	7 (22.6)
MYCN amplification				0.518
Non-amplified	91 (88.3)	64 (88.9)	27 (87.1)
Amplified	12 (11.7)	8 (11.1)	4 (12.9)
INRGSS stage				0.706
L1	47 (45.6)	33 (45.8)	14 (45.2)
L2	56 (54.4)	39 (54.2)	17 (54.8)
Histological differentiation				0.288
Differentiated	38 (36.9)	26 (36.1)	12 (38.7)
Undifferentiated or poorly differentiated	65 (63.1)	46 (63.9)	19 (61.3)
Infiltrating across the midline				0.880
Absence	54 (52.4)	38 (52.8)	16 (51.6)
Presence	49 (47.6)	34 (47.2)	15 (48.4)
Calcification				0.960
Absence	32 (31.1)	22 (30.6)	10 (32.3)
Presence	71 (68.9)	50 (69.4)	21 (67.7)
DL-based signature, median (IQR)	-0.243 (0.154)	-0.244 (0.153)	-0.243 (0.229)	0.311
EFS, median (IQR), month	72 (78)	71 (78)	72 (78)	0.437

IQR: Interquartile range; INRGSS: International Neuroblastoma Risk Grouping Staging System; DL: Deep learning; EFS: Event-free survival.

Full Size Table

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; 17(3): 114744
URL: https://www.wjgnet.com/2218-4333/full/v17/i3/114744.htm
DOI: https://dx.doi.org/10.5306/wjco.v17.i3.114744

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]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.