机器学习模型使用血清胃生物标志物用于非侵入性预测缩性胃炎:一项比较研究
Dong Li1, Haitao Yu1, Baihan Jin1
1Department of Gastroenterology, No. 971 Hospital of the People's Liberation Army Navy, Qingdao, Shandong, China.
Frontiers in medicine
|March 6, 2026
概括
机器学习模型可以帮助非侵入性地检测慢性缩性胃炎 (CAG). 使用四个预测因子的简单模型在排除CAG方面表现强,有助于患者分拣.
科学领域:
- 胃肠病学 胃肠病学
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的机器学习
背景情况:
- 慢性缩性胃炎 (CAG) 是一种癌前病变,需要早期检测.
- 非侵入性的血清生物标志物为CAG查提供了对内镜的有希望的替代方案.
- 目前生物标志物的预测准确性不足以达到最佳水平,需要改进诊断方法.
研究的目的:
- 确定慢性缩性胃炎 (CAG) 的关键预测因素.
- 开发和比较用于CAG预测的机器学习 (ML) 模型.
- 评估ML在对患者进行内镜检查时的优势.
主要方法:
- 对222名受试者的回顾性研究,随机训练/测试组分.
- 特性选择确定了四个预测因素:PGI,PGI/PGII比率,年龄,抗H. pylori抗体.
- 通过交叉验证和持久测试集,训练和验证了8个ML模型.
主要成果:
- 包括抗H. pylori阳性在内的四个预测因素被确定为CAG.
- 弹性净和物流回归模型显示了最高的歧视 (AUC 0.823,0.810) 和出色的负预测值 (>0.95).
- 简单的线性模型表现优于复杂的,过度装配的基于树的模型,并提供了卓越的临床实用性.
结论:
- 简单的,可解释的模型 (弹性网,物流回归) 有效地识别CAG的非侵入性.
- 这些模型作为强大的分类工具,特别是排除疾病.
- 在广泛临床实施之前,建议进行进一步的外部验证.
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