Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study
Abstract Background Delayed closure of a temporary ileostomy in patients with rectal cancer may cause psychological, physiological, and socioeconomic burdens to patients. Purpose This study aimed to develop and validate a machine learning-based model to predict the delayed ileostomy closure after su...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | World Journal of Surgical Oncology |
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| Online Access: | https://doi.org/10.1186/s12957-025-03843-w |
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| author | Jianying Liu Mengxiao Jiang Xiaoping Chen Yonglan Ge Zongxin Zheng Xia Yang Wenhao Zhou Huiting Zhang Meichun Zheng Baojia Luo |
| author_facet | Jianying Liu Mengxiao Jiang Xiaoping Chen Yonglan Ge Zongxin Zheng Xia Yang Wenhao Zhou Huiting Zhang Meichun Zheng Baojia Luo |
| author_sort | Jianying Liu |
| collection | DOAJ |
| description | Abstract Background Delayed closure of a temporary ileostomy in patients with rectal cancer may cause psychological, physiological, and socioeconomic burdens to patients. Purpose This study aimed to develop and validate a machine learning-based model to predict the delayed ileostomy closure after surgery in patients with rectal cancer. Design A retrospective study. Methods LASSO regression was used for feature screening, and XGBoost was used for machine learning model construction. Model performance was assessed by receiver operating characteristic (ROC) curve analysis, calibration curve analysis, clinical decision curve analysis, sensitivity, specificity, accuracy, and F1 score. The SHAP method was used to interpretate the results of the machine learning model. Results A total of 442 rectal cancer patients who received a loop ileostomy were included in this study, and 305 experienced delayed closure (69%). The XGBoost model area under the ROC curve (AUC) of the training set was 0.744 (95% confidence interval [CI]: 0.686–0.806) and of the test set was 0.809 (95% CI: 0.728–0.889). The importance of each variable, in descending order was body mass index (BMI), postoperative chemotherapy, distance from tumor to anal margin, depth of tumor infiltration, neoadjuvant chemoradiotherapy, and anastomotic stenosis. The importance of SHAP variables in the model from high to low was: ‘BMI’ ‘postoperative chemotherapy’ ‘distance of the tumor from the anal verge’ ‘depth of tumor infiltration’ ‘neoadjuvant radiotherapy’ ‘anastomotic stenosis’. Conclusion The XGBoost machine learning model we constructed showed good performance in predicting delayed closure of loop ileostomy in rectal cancer patients. In addition, the SHAP method can help better understand the results of machine learning models. |
| format | Article |
| id | doaj-art-5a9f566335874c6398f7fa10cc4eeccf |
| institution | OA Journals |
| issn | 1477-7819 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Surgical Oncology |
| spelling | doaj-art-5a9f566335874c6398f7fa10cc4eeccf2025-08-20T02:32:04ZengBMCWorld Journal of Surgical Oncology1477-78192025-05-0123111210.1186/s12957-025-03843-wMachine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective studyJianying Liu0Mengxiao Jiang1Xiaoping Chen2Yonglan Ge3Zongxin Zheng4Xia Yang5Wenhao Zhou6Huiting Zhang7Meichun Zheng8Baojia Luo9Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Urinary Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Urinary Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Urinary Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterNursing Department of Huangpu Yard, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterDepartment of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer CenterAbstract Background Delayed closure of a temporary ileostomy in patients with rectal cancer may cause psychological, physiological, and socioeconomic burdens to patients. Purpose This study aimed to develop and validate a machine learning-based model to predict the delayed ileostomy closure after surgery in patients with rectal cancer. Design A retrospective study. Methods LASSO regression was used for feature screening, and XGBoost was used for machine learning model construction. Model performance was assessed by receiver operating characteristic (ROC) curve analysis, calibration curve analysis, clinical decision curve analysis, sensitivity, specificity, accuracy, and F1 score. The SHAP method was used to interpretate the results of the machine learning model. Results A total of 442 rectal cancer patients who received a loop ileostomy were included in this study, and 305 experienced delayed closure (69%). The XGBoost model area under the ROC curve (AUC) of the training set was 0.744 (95% confidence interval [CI]: 0.686–0.806) and of the test set was 0.809 (95% CI: 0.728–0.889). The importance of each variable, in descending order was body mass index (BMI), postoperative chemotherapy, distance from tumor to anal margin, depth of tumor infiltration, neoadjuvant chemoradiotherapy, and anastomotic stenosis. The importance of SHAP variables in the model from high to low was: ‘BMI’ ‘postoperative chemotherapy’ ‘distance of the tumor from the anal verge’ ‘depth of tumor infiltration’ ‘neoadjuvant radiotherapy’ ‘anastomotic stenosis’. Conclusion The XGBoost machine learning model we constructed showed good performance in predicting delayed closure of loop ileostomy in rectal cancer patients. In addition, the SHAP method can help better understand the results of machine learning models.https://doi.org/10.1186/s12957-025-03843-wMachine learningRectal cancerLoop ileostomyDelayed closure |
| spellingShingle | Jianying Liu Mengxiao Jiang Xiaoping Chen Yonglan Ge Zongxin Zheng Xia Yang Wenhao Zhou Huiting Zhang Meichun Zheng Baojia Luo Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study World Journal of Surgical Oncology Machine learning Rectal cancer Loop ileostomy Delayed closure |
| title | Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study |
| title_full | Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study |
| title_fullStr | Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study |
| title_full_unstemmed | Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study |
| title_short | Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study |
| title_sort | machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery a retrospective study |
| topic | Machine learning Rectal cancer Loop ileostomy Delayed closure |
| url | https://doi.org/10.1186/s12957-025-03843-w |
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