Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning

doi:10.4240/wjgs.v17.i9.107977

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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 Study

World J Gastrointest Surg. Sep 27, 2025; 17(9): 107977
Published online Sep 27, 2025. doi: 10.4240/wjgs.v17.i9.107977

Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning

Wang-Shuo Yang, Yang Su, Yan-Qi Li, Jun-Bo Hu, Meng-Die Liu, Lu Liu

Wang-Shuo Yang, Yan-Qi Li, Jun-Bo Hu, Lu Liu, Department of Gastrointestinal Surgery Center, Tongji Hospital, Wuhan 430060, Hubei Province, China

Yang Su, Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan 430071, Hubei Province, China

Meng-Die Liu, Department of Biliary-Pancreatic Surgery, Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China

Co-corresponding authors: Yang Su and Meng-Die Liu.

Author contributions: Yang WS wrote the first draft, developed the main ideas, curated the data, designed the methodology, created visualizations, and contributed to review and editing; Su Y developed the main ideas, designed the methodology, performed formal analysis, and contributed to review and editing; Li YQ curated the data and performed formal analysis; Hu JB acquired the funding; Liu L acquired the funding and contributed to review and editing; Liu MD curated the data and supervised the project. This study is a multidisciplinary collaborative project involving clinical surgery, artificial intelligence algorithms, and data analysis. Professor Su Y was responsible for the clinical design and acquisition of patient data, ensuring the clinical relevance and feasibility of the research. Liu MD led the development and optimization of the machine learning models, playing a key role in model selection and performance evaluation. All two authors played indispensable roles in the study and are required to communicate externally to address academic inquiries and technical details. Therefore, designating all three as corresponding authors allows for more comprehensive responses to peer reviews and post-publication queries, ultimately enhancing the quality and impact of the paper.

Institutional review board statement: The study was conducted in strict accordance with the principles outlined in the Declaration of Helsinki and received ethical approval from the local Ethics Committee at Tongji Hospital, Huazhong University of Science and Technology. All procedures adhered to relevant regulations and guidelines. The study was approved under protocol number 202410039, with ethical clearance granted on October 8, 2024.

Informed consent statement: Given the retrospective nature of the study, the requirement for informed consent was waived. To ensure patient confidentiality, all datasets were de-identified.

Conflict-of-interest statement: The authors declare no conflict of interest.

Data sharing statement: The data utilized in this study can be obtained from the corresponding author upon reasonable request.

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: Yang Su, Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan 430071, Hubei Province, China. yangsueinfo@163.com

Received: April 2, 2025
Revised: May 14, 2025
Accepted: July 31, 2025
Published online: September 27, 2025
Processing time: 175 Days and 19.9 Hours

Abstract

BACKGROUND

Parastomal hernia (PSH) is a common and challenging complication following preventive ostomy in rectal cancer patients, lacking accurate tools for early risk prediction.

AIM

To explore the application of machine learning algorithms in predicting the occurrence of PSH in patients undergoing preventive ostomy after rectal cancer resection, providing valuable support for clinical decision-making.

METHODS

A retrospective analysis was conducted on the clinical data of 579 patients who underwent rectal cancer resection with preventive ostomy at Tongji Hospital, Huazhong University of Science and Technology, between January 2015 and June 2023. Various machine learning models were constructed and trained using preoperative and intraoperative clinical variables to assess their predictive performance for PSH risk. SHapley Additive exPlanations (SHAP) were used to analyze the importance of features in the models.

RESULTS

A total of 579 patients were included, with 31 (5.3%) developing PSH. Among the machine learning models, the random forest (RF) model showed the best performance. In the test set, the RF model achieved an area under the curve of 0.900, sensitivity of 0.900, and specificity of 0.725. SHAP analysis revealed that tumor distance from the anal verge, body mass index, and preoperative hypertension were the key factors influencing the occurrence of PSH.

CONCLUSION

Machine learning, particularly the RF model, demonstrates high accuracy and reliability in predicting PSH after preventive ostomy in rectal cancer patients. This technology supports personalized risk assessment and postoperative management, showing significant potential for clinical application. An online predictive platform based on the RF model (https://yangsu2023.shinyapps.io/parastomal_hernia/) has been developed to assist in early screening and intervention for high-risk patients, further enhancing postoperative management and improving patients’ quality of life.

Keywords: Machine learning; Rectal cancer; Parastomal Hernia; SHapley Additive exPlanation algorithms; Predictive model

Core Tip: This research proposed and validated a predictive model based on machine learning techniques to assess the risk of parastomal hernia following prophylactic ostomy in individuals with rectal cancer. Among multiple algorithms, the random forest (RF) model achieved the best performance. SHapley Additive exPlanations identified tumor distance from the anal verge, body mass index, and preoperative hypertension as key predictors. An online risk prediction tool based on the RF model has been created to support early screening and individualized postoperative management, offering practical value for clinical decision-making.