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 1 Computed tomography scanners and parameters

Scanning models	Tube voltage (kV)	Tube current (mA)	Acquisition matrix	Pitch (mm)	Slice thickness (mm)	Slice interval (mm)
Siemens SOMATOM Force computed tomography	120	200	512 × 512	0.6	5	5
United Imaging uCT 710 (64-slice)	120	108	512 × 512	1.0	5	5
Brilliance 64	120	200	512 × 512	0.8	5	5

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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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