Explainable Machine Learning for Predicting Early Gastric Cancer: a Retrospective Cohort Study
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 10
- 试验地点
- 1
- 主要终点
- Explainable machine learning for predicting early gastric cancer
研究概览
简要总结
Abstract Background: Early detection of gastric cancer is crucial for improving patient survival rates. Currently, the primary method for diagnosing early-stage gastric cancer is endoscopy, which has various limitations. Additionally, single laboratory tests continue to fall short of the requirements for early screening. This study aims to develop a machine learning (ML) model using clinical data to predict early-stage gastric cancer and apply SHapley Additive exPlanation (SHAP) values to explain the ML model.
Methods: This study involved patients who provided gastric tissue samples at Wenzhou Central Hospital from 2019 to 2023. The investigators gathered various laboratory test results from these patients. The investigators constructed and evaluated nine ML models to predict early-stage gastric cancer, using the area under the curve (AUC), accuracy, and sensitivity to assess their performance. For the most effective prediction model, The investigators utilized the SHAP method to determine the features' importance and explain the ML model.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •all patients with a gastric tissue pathology result are included
排除标准
- •unclear or incomplete pathology results
- •significant missing laboratory data
- •progressive and advanced gastric cancer
结局指标
主要结局
Explainable machine learning for predicting early gastric cancer
时间窗: From June 2025 to July 2025
The area under the ROC curve (AUC) was used as the primary outcome measure
次要结局
- Explainable machine learning for predicting early gastric cancer(From June 2025 to July 2025)
研究者
sunmeng chen
Resident in gastrointestinal surgery
Wenzhou Central Hospital