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World J Gastroenterol. Mar 21, 2026; 32(11): 116220
Published online Mar 21, 2026. doi: 10.3748/wjg.v32.i11.116220
Published online Mar 21, 2026. doi: 10.3748/wjg.v32.i11.116220
Explainable machine learning model integrating clinical and radiomic features for predicting acute suppurative cholecystitis
Guo-Dong Chen, Yu-Hua Ge, Department of Radiology, Panjin Liaohe Oilfield Gem Flower Hospital, Panjin 124010, Liaoning Province, China
Bai-Qing Chen, Department of Radiology, The People’s Hospital of Liaoning Province, Shenyang 110067, Liaoning Province, China
Ji-Liang Liu, Department of Ophthalmology, Zigong Fourth People’s Hospital, Zigong 643000, Sichuan Province, China
Kai-Wen Cheng, Shenyang Pharmaceutical University, Shenyang 110016, Liaoning Province, China
Han-Wei Xiao, Beijing Anzhen Nanchong Hospital of Capital Medical University and Nanchong Central Hospital, Nanchong 63700, Sichuan Province, China
Hong-Yu Long, Feng Xie, Department of Interventional Medicine, Jin Qiu Hospital of Liaoning Province, Shenyang 110016, Liaoning Province, China
Co-first authors: Guo-Dong Chen and Bai-Qing Chen.
Co-corresponding authors: Hong-Yu Long and Feng Xie.
Author contributions: Chen GD and Chen BQ contributed equally to this manuscript; Chen GD and Chen BQ collected the papers and analyzed data, analyzed the conclusions, and drafted the manuscript; Ge YH, Liu JL, Cheng KW, and Xiao HW presented the idea of this paper, reviewed the data and conclusions; Long HY and Xie F analyzed the conclusions, and drafted and revised the manuscript; Xie F and Long HY are the corresponding authors; all authors read and approved the final manuscript.
Institutional review board statement: Institutional review board approval was obtained from the Institutional Review Board of the People’s Hospital of Liaoning Province (No. 2023K047), the Institutional Review Board of Panjin Liaohe Oilfield Gem Flower Hospital (No. LLSC-2025-LW-01), and the Institutional Review Board of Nanchong Central Hospital (No. 2025-125).
Informed consent statement: Given the retrospective design of this study, a waiver of participant informed consent was granted by the Institutional Review Board of the People’s Hospital of Liaoning Province, the Institutional Review Board of Panjin Liaohe Oilfield Gem Flower Hospital, and the Institutional Review Board of Nanchong Central Hospital.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: Anonymized data not presented herein is available upon reasonable request from the corresponding author on rational request by any qualified researcher.
Corresponding author: Feng Xie, MD, Doctor, Department of Interventional Medicine, Jin Qiu Hospital of Liaoning Province, No. 317 Xiaonan Road, Shenhe District, Shenyang 110016, Liaoning Province, China. 15040255877@163.com
Received: November 6, 2025
Revised: December 4, 2025
Accepted: January 8, 2026
Published online: March 21, 2026
Processing time: 131 Days and 6.2 Hours
Revised: December 4, 2025
Accepted: January 8, 2026
Published online: March 21, 2026
Processing time: 131 Days and 6.2 Hours
Core Tip
Core Tip: This multi-center study developed and validated a fusion model to preoperatively predict acute suppurative cholecystitis (ASC). By integrating clinical characteristics and computed tomography radiomics features using a stacking ensemble strategy, the fusion model achieved an area under the receiver operating characteristic curve (AUC) of 0.82 on the external test dataset, significantly outperforming the clinical (AUC = 0.75) and radiomics (AUC = 0.76) models alone. It also showed higher sensitivity (71.4%) while maintaining high specificity (83.1%). The study concludes that this clinical-radiomics model can significantly improve the predictive accuracy for ASC, aiding in better surgical planning and risk assessment.