Interpretable radiomics model based on magnetic resonance imaging to predict responses to transarterial chemoembolization for hepatocellular carcinoma

doi:10.4329/wjr.v18.i2.116486

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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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©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.

World J Radiol. Feb 28, 2026; 18(2): 116486
Published online Feb 28, 2026. doi: 10.4329/wjr.v18.i2.116486

Table 3 Radiomic features for predicting transarterial chemoembolization treatment response in hepatocellular carcinoma patients by least absolute shrinkage and selection operator regression analysis

Cohort	Feature type	Feature name
FS-T2WI	Shape feature	Elongation
	GLSZM	Low-gray level zone emphasis
	GLDM	Gray level variance
Arterial phase	GLCM	Inverse variance
Arterial phase	First-order feature	Mean
Portal vein phase	First-order feature	Mean

FS-T2WI: Fat-saturation T2-weighted imaging; GLSZM: Gray level size zone matrix; GLDM: Gray level dependence matrix; GLCM: Gray level co-occurrence matrix.

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Citation: Mao Q, Zhang P, Zhou MT, Shi Y, Min XL, Xu H, Yang L, Zhang XM. Interpretable radiomics model based on magnetic resonance imaging to predict responses to transarterial chemoembolization for hepatocellular carcinoma. World J Radiol 2026; 18(2): 116486
URL: https://www.wjgnet.com/1949-8470/full/v18/i2/116486.htm
DOI: https://dx.doi.org/10.4329/wjr.v18.i2.116486

Mao Q, Zhang P, Zhou MT, Shi Y, Min XL, Xu H, Yang L, Zhang XM. Interpretable radiomics model based on magnetic resonance imaging to predict responses to transarterial chemoembolization for hepatocellular carcinoma. World J Radiol 2026; 18(2): 116486 [PMID: 41809705 DOI: 10.4329/wjr.v18.i2.116486]

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