Construction and validation of machine learning-based prediction models for postoperative bleeding following endoscopic resection of gastric gastrointestinal stromal tumor
10.3969/j.issn.1005-202X.2025.04.018
- VernacularTitle:基于机器学习的胃间质瘤内镜手术术后出血风险预测模型的构建与验证
- Author:
Luojie LIU
1
;
Jian CHEN
;
Fuli GAO
;
Yunfu FENG
;
Xiaodan XU
Author Information
1. 苏州大学附属常熟医院(常熟市第一人民医院)消化内科,江苏常熟 215500
- Publication Type:Journal Article
- Keywords:
gastric gastrointestinal stromal tumor;
automated machine learning;
endoscopic surgery;
postoperative bleeding;
prediction model
- From:
Chinese Journal of Medical Physics
2025;42(4):550-560
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the risk factors for postoperative bleeding after endoscopic resection of gastric gastrointestinal stromal tumor(gGIST)and to construct prediction models using 4 different machine learning algorithms for accurately predicting postoperative bleeding.Methods The clinical data of gGIST patients were collected,and the patients were randomly divided into a training cohort(n=502)and a validation cohort(n=130)at an 8:2 ratio.Synthetic minority over-sampling technique-nominal continuous was used for oversampling in the training cohort.Four prediction models were constructed using gradient boost machine(GBM),deep learning,generalized linear model and distributed random forest,separately;and in addition,the least absolute shrinkage and selection operator was used to screen variables and construct a traditional Logistic regression model.Model performance was evaluated by calculating the area under the receiver operating characteristic curve(AUC),sensitivity,specificity,accuracy,positive predictive value and negative predictive value.Interpretability analyses,including feature importance,SHapley additive exPlanation and force plot,were performed on the optimal model,and a practically applicable web application was developed.Results Among 632 patients,78(12.3%)experienced postoperative bleeding.In the validation cohort,GBM model performed best among 5 prediction models,with an AUC value of 0.889 and a 95%CI of 0.829-0.948,superior to the other 4 models.Variable importance analysis identified surgeon experience,operation time,intraoperative hemorrhage,tumor size as the factors affecting postoperative bleeding prediction.The SHapley additive exPlanation plot and force plot showed the distribution characteristics of variables in the binary classification prediction results and the effect of each variable on the prediction results.Conclusion GBM model has high predictive value for postoperative bleeding following endoscopic resection of gGIST,and the construction of the web application facilitates its clinical use.
