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

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.