Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography

doi:10.4240/wjgs.v18.i5.115903

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World Journal of Gastrointestinal Surgery

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1948-9366

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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 Gastrointest Surg. May 27, 2026; 18(5): 115903
Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.115903

Table 6 Optimal radiomic features of pancreatic parenchyma

Feature number	Optimal radiomic features
1	Wavelet-LLL_glcm_Correlation_qcut
2	Wavelet-HHL_gldm_DependenceVariance_qcut
3	Original_shape_Maximum2DDiameterRow_qcut
4	Wavelet-LHL_glszm_SmallAreaEmphasis_qcut
5	Wavelet-LHL_glszm_SmallAreaEmphasis_cut
6	Logarithm_firstorder_10Percentile_qcut
7	Log-sigma-5-0-mm-3D_glcm_Imc1_cut
8	Gradient_gldm_GrayLevelVariance_cut
9	Wavelet-LHL_firstorder_Mean_qcut
10	Log-sigma-5-0-mm-3D_glcm_Imc1_qcut

The “_cut” indicates equal-width binning; “_qcut” indicates equal-frequency binning.

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Citation: Feng Y, Hu XH, Xiao B. Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography. World J Gastrointest Surg 2026; 18(5): 115903
URL: https://www.wjgnet.com/1948-9366/full/v18/i5/115903.htm
DOI: https://dx.doi.org/10.4240/wjgs.v18.i5.115903

Feng Y, Hu XH, Xiao B. Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography. World J Gastrointest Surg 2026; 18(5): 115903 [DOI: 10.4240/wjgs.v18.i5.115903]

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