Construction and validation of machine learning-based prediction models for postoperativebleeding following endoscopic resection of gastric gastrointestinal stromal tumor(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第4期
- Page:
- 550-560
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Construction and validation of machine learning-based prediction models for postoperativebleeding following endoscopic resection of gastric gastrointestinal stromal tumor
- Author(s):
- LIU Luojie1; CHEN Jian1; GAO Fuli1; FENG Yunfu2; XU Xiaodan1
- 1. Department of Gastroenterology, Changshu No.1 People’s Hospital/Changshu Hospital Affiliated to Soochow University, Changshu215500, China; 2. Endoscopy Center, the First People’s Hospital of Kunshan, Kunshan 215300, China
- Keywords:
- gastric gastrointestinal stromal tumor; automated machine learning; endoscopic surgery; postoperative bleeding; prediction model
- PACS:
- R318；R573
- DOI:
- 10.3969/j.issn.1005-202X.2025.04.018
- Abstract:
- Objective To explore the risk factors for postoperative bleeding after endoscopic resection of gastricgastrointestinal stromal tumor (gGIST) and to construct prediction models using 4 different machine learning algorithms foraccurately predicting postoperative bleeding. Methods The clinical data of gGIST patients were collected, and the patientswere randomly divided into a training cohort (n=502) and a validation cohort (n=130) at an 8:2 ratio. Synthetic minority oversampling technique-nominal continuous was used for oversampling in the training cohort. Four prediction models wereconstructed 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 atraditional Logistic regression model. Model performance was evaluated by calculating the area under the receiver operatingcharacteristic 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 theoptimal 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 identifiedsurgeon experience, operation time, intraoperative hemorrhage, tumor size as the factors affecting postoperative bleedingprediction. The SHapley additive exPlanation plot and force plot showed the distribution characteristics of variables in thebinary classification prediction results and the effect of each variable on the prediction results. Conclusion GBM model hashigh predictive value for postoperative bleeding following endoscopic resection of gGIST, and the construction of the webapplication facilitates its clinical use.
Last Update: 2025-04-30