一种新的IagPLS基线校正方法用于用拉曼光谱识别质瘤
Yiran Shao1, Lipu Zhou1, Yan Zhou2
1School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Beijing 100191, China. 07935@buaa.edu.cn.
Analytical methods : advancing methods and applications
|June 20, 2025
概括
一种新的改进的自适应梯度衍生惩罚最小方程 (IagPLS) 方法增强了拉曼光谱技术,用于实时质瘤诊断. 这种技术显著提高了手术内光学活检的准确性和速度.
科学领域:
- 生物医学工程 生物医学工程
- 频谱学是一种光谱学.
- 计算生物学 计算生物学
背景情况:
- 质瘤是一种具有攻击性的中枢神经系统瘤,需要先进的诊断工具.
- 拉曼光谱提供了非侵入性的分子指纹,但受到自光干扰的影响.
- 现有的基线校正方法无法充分解决噪声,特征保存和光谱异质性问题.
研究的目的:
- 开发一种改进的基线校正方法,用于脑组织的拉曼光谱.
- 用拉曼光谱来提高手术内质瘤诊断的准确性和效率.
- 为光学活检系统提供强大的预处理工具.
主要方法:
- 提出了一种改进的自适应梯度衍生惩罚最小方程 (IagPLS) 方法.
- 集成曲率驱动的动态调整,SHAP算法引导的特征保护和量子灵感的全球优化.
- 在423个临床拉曼光谱 (157个正常,266个质瘤组织) 上验证了该方法.
主要成果:
- 通过随机森林分类,IagPLS实现了96.1%的质瘤识别准确度,表现优于airPLS (89.4%) 和agdPLS (87.0%).
- 显著改善了光谱特征峰值突出 (82.05%与agdPLS相比) 并减少了负余面积 (89.79%与airPLS相比).
- 证明了快速处理 (每次校正<0.1秒) 和增强的生物解释性.
结论:
- IagPLS是一种高级的光谱校正技术,用于通过拉曼光谱对手术内质瘤进行诊断.
- 该方法为光学活检系统提供了更好的准确性,速度和可解释性.
- 该算法框架可适应用于精准医学中的多模式生物医学光谱分析.
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