Application Of Surface-Enhanced Raman Spectroscopy In Early Lung Nodule Screening

Lung cancer is the fastest growing cancer in recent decades,and the mortality rate is currently the highest among malignant tumors,and there is an urgent need for a method that can be carried out quickly and conveniently for lung cancer screening.The role of surface-enhanced Raman spectroscopy in cancer screening has been discussed and studied by many scholars,which has the advantages of rapid sample measurement,low sample requirements and high accuracy.Human serum is often used as a test sample because it contains rich substance information and is stable and not susceptible to environmental influences.Due to the different degree of metabolism between patients and healthy people,the characteristic substances and content contained in serum will be different,which are the theoretical basis for early screening of lung cancer based on human serum samples.In this thesis,surface enhanced Raman spectroscopy was taken as the main research method,human serum was taken as the research object,and the data analysis and classification methods such as principal component analysis-linear discriminant analysis(PCA-LDA),partial least squares discriminant analysis(PLS-DA),principal component analysis-support vector machine(PCA-SVM)were used to classify serum from patients with malignant pulmonary nodules,benign lung nodule serum and normal human serum,so as to establish an early screening model for lung cancer.This thesis mainly carries out the following three parts:The first part is to prepare metal nanoparticles and dielectric microspheres to form a metal-dielectric composite nanomaterial substrate,and mix and dry them with serum samples for SERS detection.The positioning of dielectric microspheres in nanoclusters will enhance the electromagnetic field between the precious metals,and correspondingly enhance the Raman signal of the sample adsorbed on the surface of the precious metal.The second part is to use the base made above for Raman detection in patients with malignant pulmonary nodules,benign pulmonary nodules,and normal human patients.A total of 179 serum samples were collected from Sichuan Cancer Hospital,including58 malignant patients,58 benign patients and 63 healthy people.A clear,stable and highly repeatable spectrum of human serum was obtained.The third part is to preprocess,classify the spectral data,and to establish one model.The mean serum SERS spectra of the normal group and the malignant group/benign group were significantly different,and the mean serum SERS spectra of the malignant group and the benign group were slightly different.The serum spectra of the three groups of samples were classified by PCA-LDA,PLS-DA and PCA-SVM,and the classification model of lung cancer screening was established to assist the diagnosis of early screening of lung cancer.Among them,the prediction classification accuracy of PCA-LDA,PLS-DA and PCA-SVM models was 79.17%,82.5% and 93.33%.