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 7 Optimal radiomic features of peripancreatic necrotic collections

Feature number	Optimal radiomic features
1	Wavelet-HHH_glszm_GrayLevelNonUniformity_qcut
2	Log-sigma-3-0-mm-3D_glcm_Idm_qcut
3	Original_glszm_ZoneEntropy_qcut
4	Square_gldm_DependenceVariance_qcut
5	Exponential_gldm_DependenceVariance_qcut
6	Wavelet-LLL_glrlm_LongRunLowGrayLevelEmphasis_qcut
7	Log-sigma-5-0-mm-3D_glcm_Idmn_qcut
8	Log-sigma-2-0-mm-3D_firstorder_10Percentile_qcut
9	Original_shape_Flatness_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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