无标签血清SERS与RFE-GBDT算法相结合,用于对肝癌进行非侵入性查
Jingjing Gao1, Tianyi Lv1, Xianqiong Gong2
1School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 9, 2026
概括
这项研究引入了一种使用表面增强拉曼光谱 (SERS) 和深度学习进行准确肝癌分期的非侵入性光学诊断方法. 这种方法对早期检测和辅助查有希望,改善了患者的治疗结果.
科学领域:
- 生物医学光学 生物医学光学
- 频谱学是一种光谱学.
- 人工智能在医学中的应用
背景情况:
- 早期诊断肝癌至关重要,但目前的方法是侵入性和不切实际的查.
- 对于早期检测有效的非侵入性诊断技术有很大的需求.
- 表面增强拉曼光谱 (SERS) 提供了在生物样本中分子指纹的潜力.
研究的目的:
- 开发和验证一种非侵入性光学诊断方法用于肝癌分期.
- 将SERS与深度学习算法集成在一起,以准确地分类肝癌阶段.
- 评估这种方法作为早期肝癌查的辅助工具的有效性.
主要方法:
- 从肝癌患者 (T1,T2,T3阶段),乙型肝炎患者和健康对照患者的血清样本收集SERS光谱数据.
- 使用递归特征消除 (RFE) 来从SERS频谱中进行最佳特征选择.
- 使用渐变增强决策树 (GBDT) 模型,通过剩余代学习进行优化,用于分类.
主要成果:
- RFE-GBDT模型在五个类别分类中 (肝癌阶段,HBV,正常) 实现了92.68%的高精度.
- 使用RFE进行特征选择,通过保留相关的光谱频段,显著提高了模型的辨别能力.
- 局部可解释的模型不可知解释 (LIME) 用于解释关键的光谱带,提供生物学见解.
结论:
- 结合SERS和RFE-GBDT深度学习算法,为肝癌诊断提供了一个有前途的非侵入性工具.
- 这种方法表明了有效的辅助查和肝癌分期的潜力.
- 开发的方法比传统的侵入性诊断技术有了显著的进步.
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