Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Jun 27, 2025; 17(6): 106155
Published online Jun 27, 2025. doi: 10.4240/wjgs.v17.i6.106155
Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia
Da-Lue Li, Ling Zhu, Shun-Li Liu, Zhi-Bo Wang, Jing-Nong Liu, Xiao-Ming Zhou, Ji-Lin Hu, Rui-Qing Liu
Da-Lue Li, Department of Emergency, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Ling Zhu, Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Shun-Li Liu, Xiao-Ming Zhou, Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Zhi-Bo Wang, Department of General Surgery, Weifang People’s Hospital, Weifang 261000, Shandong Province, China
Jing-Nong Liu, Ji-Lin Hu, Department of Gastroenterological Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Rui-Qing Liu, Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Co-corresponding authors: Ji-Lin Hu and Rui-Qing Liu.
Author contributions: Li DL contributed to conceptualization and writing-original draft; Zhu L contributed to software; Liu SL contributed to validation; Wang ZB contributed to visualization and investigation; Liu JN contributed to date curation; Zhou XM contributed to supervision; Hu JL contributed to methodology; Liu RQ contributed to writing-reviewing and editing; All authors have read and approve the final manuscript.
Supported by the National Natural Science Foundation of China, No. 82000482; China Postdoctoral Science Foundation funded, No. 2023M741858; and China Crohn’s and Colitis Foundation, No. CCCF-QF-2023C18-3.
Institutional review board statement: The study was reviewed and approved by the ethics committee of the Affiliated Hospital of Qingdao University (Approval No. QYFYWZLL29121).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at ldl199207@163.com. Participants gave informed consent for data sharing.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rui-Qing Liu, MD, Doctor, Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16 Shinan Jiangsu Road, Qingdao 266000, Shandong Province, China. liuruiqing@qdu.edu.cn
Received: February 19, 2025
Revised: April 5, 2025
Accepted: May 12, 2025
Published online: June 27, 2025
Processing time: 101 Days and 0.7 Hours
Core Tip

Core Tip: This study developed an innovative radiomic-clinical nomogram to predict bowel resection risks in patients with incarcerated inguinal hernia (IIH). By extracting 13 radiomic features from unenhanced computed tomography scans and combining them with clinical data, a predictive model was created. The nomogram showed strong performance with area under the curves of 0.864 in the training set and 0.800 in the test set. Decision curve analysis demonstrated that the integrated model outperformed standalone clinical and radiomic approaches, offering a valuable tool for improving clinical decision-making in IIH patient management.