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

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

Table 4 Radiomic features of the pancreatic parenchyma

Feature type	Feature count	Median ICC	IQR	ICC < 0.75 (%)
First-order statistics	316	0.959	0.891-0.985	17.7% (56/316)
Shape features	14	0.944	0.937-0.971	7% (1/14)
GLCM features	408	0.928	0.834-0.974	23.5% (96/408)
GLSZM features	272	0.745	0.581-0.889	52.2% (142/272)
GLRLM features	272	0.894	0.744-0.960	25.4% (69/272)
GLDM features	238	0.902	0.728-0.966	28.2% (67/238)
Total	1520	0.912	0.730-0.972	28.3% (431/1520)

GLCM: Gray-level co-occurrence matrix; GLSZM: Gray-level size zone matrix; GLRLM: Gray-level run-length matrix; GLDM: Gray-level dependence matrix; ICC: Intraclass correlation coefficient; IQR: Interquartile rage.

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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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