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World J Gastrointest Surg. May 27, 2026; 18(5): 115903
Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.115903
Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.115903
Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography
Yue Feng, Bo Xiao, Department of Radiology, Bishan Hospital of Chongqing Medical University, Bishan Hospital of Chongqing, Chongqing 402760, China
Yue Feng, Xi-Hao Hu, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Co-first authors: Yue Feng and Xi-Hao Hu.
Author contributions: Feng Y contributed to writing the first draft of the manuscript; Feng Y and Hu XH contributed to data collection, data analysis, and they contributed equally to this manuscript and are co-first authors; Feng Y, Hu XH, and Xiao B contributed to the study conception and design. All authors commented on previous versions of the manuscript and have read and approve the final manuscript.
AI contribution statement: AI tools (DeepSeek and DeepL) were used solely for linguistic refinement and formatting assistance. No AI tool participated in the study design, which was entirely developed by the authors. However, DeepSeek assisted in interpreting some results by offering suggestions for contextualizing findings within the broader literature during the writing of the Discussion section. The authors critically evaluated all suggestions and retained full control over the final interpretation.
Supported by Chongqing Science and Health Joint Medical Research Project, No. 2024MSXM165; Chongqing Bishan District Science and Technology Bureau Project, No. BSKJ2024062; and Leading Scientific Research and Innovation Team Project of Bishan Hospital of Chongqing, No. BYKY-CX2024001.
Institutional review board statement: This retrospective study was approved by the Ethics Committee of Bishan Hospital of Chongqing (Approval No. cqbykyll-20240918-108) and the Affiliated Hospital of North Sichuan Medical College (Approval No. 2024ER721-1). The study was conducted in accordance with the Declaration of Helsinki.
Informed consent statement: Due to the retrospective nature of the study, the institutional review boards waived the need for obtaining informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: No additional data to share.
Corresponding author: Bo Xiao, MD, Associate Professor, Chief, Chief Physician, Director, Department of Radiology, Bishan Hospital of Chongqing Medical University, Bishan Hospital of Chongqing, No. 9 Shuangxing Avenue, Bishan District, Chongqing 402760, China.
Received: October 31, 2025
Revised: December 16, 2025
Accepted: February 4, 2026
Published online: May 27, 2026
Processing time: 209 Days and 6.9 Hours
Revised: December 16, 2025
Accepted: February 4, 2026
Published online: May 27, 2026
Processing time: 209 Days and 6.9 Hours
Core Tip
Core Tip: Acute necrotizing pancreatitis (ANP), a more severe form of acute pancreatitis, requires early diagnosis and accurate severity stratification for optimal patient prognosis and treatment. This study demonstrates that radiomics based on contrast-enhanced computed tomography of both pancreatic parenchyma and peripancreatic necrotic collections, combined with machine learning algorithms, can effectively differentiate between severe and moderately severe ANP. This model may serve as a valuable adjunct clinical decision support tool, and its refined classification of ANP into severe and moderately severe categories could help optimize resource allocation and improve patient triage.