早期喉癌血清成分的多变量特征分析,使用表面增强的拉曼光谱学
Mingyun Gu1, Bo Yu1, Hao Liu2
1Department of Otorhinolaryngology of the Second Hospital, Dalian Medical University, Dalian 116023, LiaoNing Province, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 25, 2026
概括
早期检测喉癌至关重要. 表面增强的拉曼光谱 (SERS) 与1D-CNN深度学习模型相结合,在血清查中显示出高精度,优于传统方法,改善了患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 喉癌是一种流行的头部瘤,发病率可变.
- 由于非特异性早期症状导致诊断延迟,导致晚期和预后不佳.
- 有效的早期查对于提高生存率至关重要.
研究的目的:
- 评估表面增强拉曼光谱 (SERS) 与深度学习相结合的临床实用性,用于早期喉癌血清查.
- 将1D-CNN模型 (CNN注意力,CNN基线) 与传统机器学习算法的性能进行比较.
主要方法:
- 血清样本从早期喉癌患者,声多患者和健康对照组中收集.
- 使用SERS获取血清分子振动指纹光谱.
- 五个模型 (CNN-attention,CNN-baseline,SVM,AdaBoost,KNN) 通过Leave-One-Patient-Out交叉验证 (LOPO-CV) 进行了培训和验证.
主要成果:
- 与传统的机器学习模型相比,1D-CNN模型显示出更高的歧视性能.
- 在CNN注意力模型实现了89.33%的患者级准确性,在CNN基线模型实现了88.67%的准确性.
- 在SVM,KNN和AdaBoost模型中,歧视效率较低.
结论:
- 血清SERS与1D-CNN模型相结合,为早期喉癌查提供了一种高效和准确的方法.
- 这种方法为临床转化提供了基础,并增强了早期检测能力.
- 这些发现支持开发用于早期喉癌诊断的新技术工具.
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