机器学习分类和生化特征在实时诊断胃腺癌使用拉曼光谱的胃腺癌
Alex Noh1, Sabrina Xin Zi Quek2, Nuraini Zailani3
1School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
Scientific reports
|January 20, 2025
概括
拉曼光谱与机器学习相结合,在内镜检查期间准确地实时检测胃腺癌. 这种创新技术可以识别癌症和良性组织之间的明显的生物分子差异,帮助早期诊断.
科学领域:
- 生物光子学 生物光子学
- 医学诊断 医学诊断 医学诊断
- 生物化学 生物化学
背景情况:
- 胃腺癌的诊断依赖于侵入性活检.
- 在内镜检查过程中需要实时,非侵入性诊断工具.
- 良性和恶性胃组织之间的生物分子差异尚未完全表征.
研究的目的:
- 为了确定良性胃组织和胃腺癌之间的生物分子差异.
- 评估基于拉曼光谱的机器学习用于胃腺癌的诊断性能.
- 评估该技术在实时内镜诊断方面的潜力.
主要方法:
- 在高风险患者的内镜检查期间,实时应用拉曼光谱.
- 分析了可疑病变和相邻的正常粘膜的光谱数据.
- 机器学习模型 (PCA,LDA) 被开发并使用离开一次的交叉验证 (LOOCV) 进行验证.
- 组织病理学诊断是黄金标准.
主要成果:
- 显著的拉曼光谱模式将腺癌与良性组织区分开来 (p < 0.001).
- 机器学习模型实现了高诊断精度 (0.905),灵敏度 (0.942),特异性 (0.787) 和AUC (0.957).
- 生物化学分析表明,腺癌组织中的蛋白质增加和脂质/水减少.
结论:
- 基于拉曼光谱的机器学习显示了作为胃腺癌实时诊断工具的巨大潜力.
- 该技术可以识别恶性瘤的独特生物分子标志.
- 进一步的验证可能会证明这是一个准确的,非侵入性的助力内镜决策.
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