Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma

doi:10.4329/wjr.v18.i4.118196

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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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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 Radiol. Apr 28, 2026; 18(4): 118196
Published online Apr 28, 2026. doi: 10.4329/wjr.v18.i4.118196

Table 1 Deep learning feature biological correlation

DL feature class	MRI sequence	Radiological interpretation
Border features	T1-weighted (T1WI)	Correlates with margin sharpness and lesion boundary irregularity
Texture features	T2-weighted (T2WI)	Reflects intratumoral heterogeneity and signal intensity variations
Internal features	T2-weighted (T2WI)	Associated with the presence of internal septations or lobulations

DL: Deep learning; MRI: Magnetic resonance imaging.

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Citation: Chowdhury U, Mahajan AA, Kavitha MS, Rajendran RL, Gangadaran P, Ahn BC. Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma. World J Radiol 2026; 18(4): 118196
URL: https://www.wjgnet.com/1949-8470/full/v18/i4/118196.htm
DOI: https://dx.doi.org/10.4329/wjr.v18.i4.118196

Chowdhury U, Mahajan AA, Kavitha MS, Rajendran RL, Gangadaran P, Ahn BC. Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma. World J Radiol 2026; 18(4): 118196 [PMID: 42057840 DOI: 10.4329/wjr.v18.i4.118196]

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