Abstract：
A novel approach is proposed leveraging surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques, principal component analysis (PCA)-centroid displacement-based nearest neighbor (CDNN). This label-free approach can identify slight abnormalities between SERS spectra of gastric lesions at different stages, offering a promising avenue for detection and prevention of precancerous lesion of gastric cancer (PLGC). The agaric-shaped nanoarray substrate was prepared using gas-liquid interface self-assembly and reactive ion etching (RIE) technology to measure SERS spectra of serum from mice model with gastric lesions at different stages, and then a SERS spectral recognition model was trained and constructed using the PCA-CDNN algorithm. The results showed that the agaric-shaped nanoarray substrate has good uniformity, stability, cleanliness, and SERS enhancement effect. The trained PCA-CDNN model not only found the most important features of PLGC, but also achieved satisfactory classification results with accuracy, area under curve (AUC), sensitivity, and specificity up to 100%. This demonstrated the enormous potential of this analysis platform in the diagnosis of PLGC.
摘要：
提出了一种利用表面增强拉曼光谱（SERS）与机器学习（ML）技术相结合的新方法。主成分分析(PCA)-基于质心位移的最近邻(CDNN)。这种无标记方法可以识别不同阶段胃部病变的SERS光谱之间的轻微异常，为检测和预防胃癌癌前病变（PLGC）提供了有希望的途径。采用气液界面自组装和反应离子刻蚀(RIE)技术制备木耳状纳米阵列基底,测量不同阶段小鼠胃部病变模型血清的SERS光谱,然后利用PCA-CDNN算法训练并构建了SERS光谱识别模型。结果表明，木耳状纳米阵列基板具有良好的均匀性，稳定性,清洁度,和SERS增强效果。经过训练的PCA-CDNN模型不仅发现了PLGC的最重要特征，而且还取得了令人满意的分类结果，曲线下面积（AUC），灵敏度,和特异性高达100%。这证明了该分析平台在PLGC诊断中的巨大潜力。
