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Automated machine learning-based model for predicting benign anastomotic strictures in patients with rectal cancer who have received anterior resection

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资源类型：
WOS体系：
Pubmed体系：

收录情况： ◇ SCIE

作者：
Su Yang[1,2] * ; Li Yanqi[1,2] * ; Chen Wenshu[3] ; Yang Wangshuo[1,2] ; Qin Jichao[1,2] ; Liu Lu[1,2,*1] ;
单位： [1]Department of Gastrointestinal Surgery Center,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430030,China [2]Molecular Medicine Center, Tongi Hospita, Tongi Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China [3]School of Computer Science(National Pilot Software Engineering School), Beijing University of Posts and Telecommunication, 100876, Beijing, China
出处：
DOI： 10.1016/j.ejso.2023.107113
ISSN：

关键词： Machine learning Rectal cancer Benign anastomotic strictures SHAP algorithms

摘要：
Benign anastomotic strictures (BAS) significantly impact patients' quality of life and long-term prognosis. However, the current. clinical practice lacks accurate tools for predicting BAS. This study aimed to develop a machine-learning model to predict BAS in patients with rectal cancer who have undergone anterior resection.Data from 1973 patients who underwent anterior resection for rectal cancer were collected. Multiple machine learning classification models were integrated to analyze the data and identify the optimal model. Model performance was evaluated using receiver operator characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The Shapley Additive exPlanation (SHAP) algorithm was utilized to assess the impact of various clinical characteristics on the optimal model to enhance the interpretability of the model results.A total of 10 clinical features were considered in constructing the machine learning model. The model evaluation results indicated that the random forest (RF)model was optimal, with the area under the test set curve (AUC: 0.888, 95% CI: 0.810-0.965), accuracy: 0.792, sensitivity: 0.846, specificity: 0.791. The SHAP algorithm analysis identified prophylactic ileostomy, operative time, and anastomotic leakage as significant contributing factors influencing the predictions of the RF model.We developed a robust machine-learning model and user-friendly online prediction tool for predicting BAS following anterior resection of rectal cancer. This tool offers a potential foundation for BAS prevention and aids clinical practice by enabling more efficient disease management and precise medical interventions.© 2023 Published by Elsevier Ltd.

基金：
National Natural Science Foundation of China(Number: 2022YFC2407304)
语种：
外文
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2022]版：
大类 | 2 区 医学
小类 | 2 区 外科 3 区 肿瘤学
最新[2025]版：
大类 | 2 区 医学
小类 | 2 区 肿瘤学 2 区 外科
JCR分区：
出版当年[2021]版：
Q1 SURGERY Q3 ONCOLOGY
最新[2023]版：
Q1 SURGERY Q2 ONCOLOGY

影响因子： 最新[2023版] 最新五年平均 出版当年[2021版] 出版当年五年平均 出版前一年[2020版] 出版后一年[2022版]

第一作者：
第一作者单位： [1]Department of Gastrointestinal Surgery Center,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430030,China [2]Molecular Medicine Center, Tongi Hospita, Tongi Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China
共同第一作者：
通讯作者：
通讯机构： [1]Department of Gastrointestinal Surgery Center,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430030,China [2]Molecular Medicine Center, Tongi Hospita, Tongi Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China [*1]Department of Gastrointestinal Surgery Center,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430030,China.
推荐引用方式(GB/T 7714)：
Su Yang,Li Yanqi,Chen Wenshu,et al.Automated machine learning-based model for predicting benign anastomotic strictures in patients with rectal cancer who have received anterior resection[J].EJSO.2023,49(12):doi:10.1016/j.ejso.2023.107113.
APA：
Su Yang,Li Yanqi,Chen Wenshu,Yang Wangshuo,Qin Jichao&Liu Lu.(2023).Automated machine learning-based model for predicting benign anastomotic strictures in patients with rectal cancer who have received anterior resection.EJSO,49,(12)
MLA：
Su Yang,et al."Automated machine learning-based model for predicting benign anastomotic strictures in patients with rectal cancer who have received anterior resection".EJSO 49..12(2023)

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