一个基于内镜缩分类的胃癌风险预测模型
Yadi Lan1, Weijia Sun1, Shen Zhong1
1Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China.
BMC cancer
|March 22, 2025
概括
使用内镜缩分类的新风险预测模型可以帮助识别患胃癌 (GC) 高风险的个体. 这种基于年龄和HP感染等因素的模型有助于早期检测和临床决策.
科学领域:
- 胃肠道学和瘤学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 胃癌 (GC) 是一个重要的全球健康问题.
- 早期检测和风险分层对于改善患者的治疗结果至关重要.
- 内镜检测结果,特别是缩模式,为GC风险提供了潜在的生物标志物.
研究的目的:
- 开发和验证胃癌风险的预测模型.
- 作为风险模型的关键组成部分,利用内镜性缩分类.
- 在GC风险预测中评估各种机器学习算法的性能.
主要方法:
- 从1156名患者回顾性数据收集 (2020年1月至2021年10月).
- 使用逻辑回归 (LR),决策树,支持向量机器和随机森林开发预测模型.
- 通过最小绝对收缩和选择操作员 (LASSO) 进行变量选择,用于LR模型.
- 使用接收器操作特征 (ROC) 曲线,预测直方图和决策曲线分析 (DCA) 的模型评估.
主要成果:
- 确定了五个关键预测因素:年龄,性别,GC的家族史,HP感染和金村-塔克莫托分类 (KTC).
- 机器学习模型实现了曲线下的面积 (AUC) 值,范围从0.762到0.974 (训练集) 和0.608到0.812 (验证集).
- 后勤回归模型在验证组中显示了最高的AUC (0.812),显示了良好的校准和临床效用.
- 为了可视化LR模型预测,构建了一个名图.
结论:
- 基于内镜缩分类的诺米图有效预测胃癌风险.
- 开发的模型有望帮助临床医生做出关于GC管理的明智决策.
- 这种工具可以增强胃癌的早期检测策略.
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