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集成的LIBS-拉曼光谱与可解释的机器学习相结合,用于黑色素瘤的生物化学表征

Muhammad Nouman Khan¹, Qingsong Zhou¹, Jiaqing Guo¹

¹State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen 518060, PR China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|February 18, 2026

概括

这项研究结合了激光诱导分解光谱 (LIBS) 和拉曼光谱来分析黑色素瘤组织. 多式联络方法实现了高准确度,为癌症评估提供了一个客观,无标签的方法.

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.
癌症的诊断癌症的诊断

背景情况:

黑色素瘤是一种致命的皮肤癌,需要改进的诊断工具.
传统的组织病理学需要客观的,无标签的补充方法.
光学光谱学为无标签的组织分析提供了潜力.

研究的目的:

应用激光诱导分解光谱法 (LIBS) 和拉曼光谱法来表征黑色素瘤.
使用极端学习机器 (ELM) 建模光谱数据,并使用夏普利增量扩展 (SHAP) 解释发现.
建立一个可解释的框架,将光谱特征与黑色素瘤中的生物化学机制联系起来.

主要方法:

分析了甲固定嵌 (FFPE) 黑色素瘤和正常人体组织.
获取LIBS和拉曼光谱并使用具有五个激活功能的ELM进行建模.
用SHAP分析来确定模型的可解释性.

主要成果:

平均准确率:LIBS (鼻腔激活) 92.43%,拉曼 (sigmoid激活) 74.41%. 平均准确率:LIBS (鼻腔激活) 92.43%,拉曼 (sigmoid激活) 74.41%. 平均准确率:LIBS (鼻腔激活) 92.43%,拉曼 (sigmoid激活) 74.41%.
在特征级融合实现了77.88%的准确性与sigmoid激活.
SHAP确定了主要的LIBS (K,Ca) 和Raman (胺基,C-C/C-N波段) 特征,以区分黑色素瘤.

结论:

通过ELM和SHAP解释的LIBS和拉曼光谱,提供客观的,无标签的黑色素瘤特征.
频谱特征与生化变化相关,如黑色素瘤中的离子失衡和蛋白质变化.
这种多模式光学方法对精确和透明的癌症评估有希望.

关键词:

极端学习的机器学习.在FFPE组织中使用FFPE组织.在LIBS中,LIBS是指LIBS.黑色素瘤是一种黑色素瘤.拉曼·拉曼·拉曼是什么意思这就是 SHAP SHAP 的意思.

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