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Updated: Jan 23, 2026

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Modeling Oral-Esophageal Squamous Cell Carcinoma in 3D Organoids
Published on: December 23, 2022
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机器学习模型基于交叉队列口腔微生物签名来诊断口腔状细胞癌
Mingchao Wang1, Yanfei Sun2, Wen Gong1
1Department of Preventive Dentistry, Qingdao Stomatological Hospital Affiliated to Qingdao University, Qingdao, China.
Frontiers in microbiology
|January 22, 2026
概括
这项研究使用唾液微生物组数据开发了口腔状细胞癌 (OSCC) 的非侵入性诊断模型. 机器学习模型准确地识别了OSCC,强调了模型选择对临床应用的重要性.
科学领域:
- 微生物组研究 微生物组研究
- 口腔瘤学 在口腔瘤学
- 诊断生物标志物 诊断生物标志物
背景情况:
- 人们越来越认识到唾液微生物组在口腔状细胞癌 (OSCC) 中的作用.
- 现有的研究缺乏跨队列研究和OSCC的非侵入性诊断模型.
- 需要一个强大的,非侵入性的OSCC诊断方法.
研究的目的:
- 研究OSCC患者和健康个体之间唾液微生物组成的差异.
- 使用唾液微生物组数据开发和验证OSCC的跨队列,非侵入性诊断模型.
- 评估各种机器学习模型的诊断性能.
主要方法:
- 分析了来自665个人的交叉队列唾液微生物组数据 (311名OSCC患者,354名健康对照).
- 在OSCC患者中鉴定出显著改变的微生物.
- 构建和验证机器学习模型 (随机森林,额外树木,梯度提升,XGBoost) 用于OSCC诊断.
主要成果:
- 在OSCC患者和健康个体之间观察到唾液微生物组成的显著差异.
- 在OSCC患者中,七种特定微生物显著减少,七种显著增加.
- 机器学习模型在属和物种层面实现了高诊断准确性 (AUC 63.1%96.9%).
结论:
- 使用唾液微生物组数据可以实现OSCC的强大,非侵入性诊断模型.
- 无论分类分辨率如何,都可以达到高诊断准确度,强调模型选择.
- 机器学习模型的选择对于OSCC诊断中的临床应用至关重要.
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