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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. Nov 27, 2026; 18(11): 123786
Published online Nov 27, 2026. doi: 10.4240/wjgs.123786
Development and validation of a postoperative prediction model for residual stones after hepatectomy in patients with hepatolithiasis
Han-Rui Shao, Di Zeng, Xin Chen, Ya Zhu, Li-Xin Liu, Song-Ling Zhao, Hao Zou
Han-Rui Shao, Xin Chen, Ya Zhu, Li-Xin Liu, Song-Ling Zhao, Hao Zou, Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, Kunming 650106, Yunnan Province, China
Di Zeng, Department of Pathology, Baoji Central Hospital, Baoji 721008, Shaanxi Province, China
Co-first authors: Han-Rui Shao and Di Zeng.
Author contributions: Shao HR, Zeng D, Chen X, Zhu Y, and Zou H conceived and designed the study; Shao HR, Zeng D, Chen X, Zhu Y, Liu LX, Zhao SL, and Zou H collected and verified the clinical data; Shao HR and Zeng D performed the statistical analyses and drafted the manuscript; Chen X, Zhu Y, Liu LX, Zhao SL, and Zou H critically reviewed and revised the manuscript; all authors read and approved the final manuscript; Shao HR and Zeng D contributed equally to this work as co-first authors.
AI contribution statement: ChatGPT (OpenAI) was utilized solely for English-language editing, structural improvements, and formatting assistance during the preparation and revision of the manuscript. All content generated with the help of AI was carefully reviewed, verified, and revised by the authors. AI tools were not used to collect research data, perform statistical analyses, interpret findings, or formulate scientific conclusions. The authors are fully responsible for the accuracy, originality, integrity, and scientific validity of the manuscript and all accompanying materials.
Supported by Yunnan Fundamental Research Projects, No. 202501AY070001-033; and Yunnan Clinical Medicine Center Research Projects, No. 2024YNLCYXZX0521.
Institutional review board statement: This study was reviewed and approved by the Medical Ethics Committee of the Second Affiliated Hospital of Kunming Medical University (approval No. Shen-PJ-Ke-2023-221).
Informed consent statement: The requirement for written informed consent was waived by the Medical Ethics Committee of the Second Affiliated Hospital of Kunming Medical University because of the retrospective nature of the study, the use of existing medical records, and the minimal risk posed to the participants. All patient data were anonymized and handled in accordance with applicable confidentiality and privacy requirements.
Conflict-of-interest statement: The authors declare that they have no conflict of interest to disclose.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: The datasets generated and/or analyzed during the current study are not publicly available because they contain potentially identifiable patient information. De-identified data may be obtained from the corresponding author upon reasonable request, subject to approval by the relevant institutional ethics committee.
Corresponding author: Hao Zou, MD, Doctor, Department of Hepatopancreatobiliary Surgery, The Second Affiliated Hospital of Kunming Medical University, No. 374 Dianmian Avenue, Wu Hua District, Kunming 650106, Yunnan Province, China. haozoukm@163.com
Received: June 3, 2026
Revised: July 22, 2026
Accepted: September 8, 2026
Published online: November 27, 2026
Processing time: 162 Days and 13.6 Hours
Core Tip

Core Tip: Residual stones remain an important and clinically consequential challenge after hepatectomy for hepatolithiasis, yet individualized tools for early postoperative risk stratification remain limited. In this retrospective cohort of 300 consecutive patients, 12 complete prediction pipelines were rigorously compared using repeated nested cross-validation within the development cohort, and least absolute shrinkage and selection operator-penalized logistic regression was selected before evaluation in a strictly held-out internal validation cohort. The final model retained only four routinely available perioperative predictors: Operative time, bilateral stone distribution, T-tube drainage, and postoperative biliary stricture documented before outcome ascertainment, and achieved an internally validated area under the receiver operating characteristic curve of 0.877 with a Brier score of 0.134 and a negative predictive value of 0.944. Its combination of parsimony, interpretability, low data-acquisition burden, and stable internally validated performance represents a practical strength for postoperative risk stratification. The model may help identify patients who warrant intensified surveillance and earlier imaging assessment, while independent external validation remains necessary before routine clinical implementation.

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