Abstract：
Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.
摘要：
根据基于表面增强拉曼光谱(SERS)的呼出气体中的相同生物标志物(例如醛)在肺癌(LC)和胃癌(GC)之间的不同诊断仍然是当前研究中的挑战。这里,证明了LC和GC的准确诊断，使用人工智能技术（AI）基于等离子体金属有机框架纳米颗粒（PMN）薄膜中呼气的SERS光谱。在具有最佳结构参数的PMN薄膜中，收集了1780个SERS光谱，其中940个光谱来自健康人(n=49)，另外440名来自LC患者（n=22），其余400名来自GC患者（n=8）。利用深度学习(DL)算法,通过人工神经网络(ANN)模型对SERS光谱进行训练,结果表明，LC和GC具有良好的识别精度，准确率超过89％。此外，结合SERS峰的信息，ANN模型中的数据挖掘成功地用于探索健康人（H）和L/GC患者呼出气的细微成分差异。这项工作在呼吸分析中实现了对多种癌症疾病的出色无创诊断，为探索基于SERS谱的疾病特征提供了新的途径。
