%0 Journal Article
%T Machine learning-driven SERS analysis platform for rapid and accurate detection of precancerous lesions of gastric cancer.
%A Cao D
%A Shi F
%A Sheng J
%A Zhu J
%A Yin H
%A Qin S
%A Yao J
%A Zhu L
%A Lu J
%A Wang X
%J Mikrochim Acta
%V 191
%N 7
%D 2024 06 22
%M 38907752
%F 6.408
%R 10.1007/s00604-024-06508-9
%X 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.