Raman spectroscopy combined with multivariate analysis to study the biochemical mechanism of lung cancer microwave ablation

Abstract

Lung cancer is the leading cause of death in cancer patients, and microwave ablation (MWA) has been extensively used in clinical treatment. In this study, we characterized the spectra of MWA-treated and untreated lung squamous cell carcinoma (LSCC) tissues, as well as healthy lung tissue, and conducted a preliminary analysis of spectral variations associated with MWA treatment. The results of characteristic spectral analysis of different types of tissues indicated that MWA treatment induces an increase in the content of nucleic acids, proteins, and lipid components in lung cancer tissues. The discriminant model based on the principal component analysis - linear discriminant analysis (PCA-LDA) algorithm together with leave-one-out cross validation (LOOCV) method yield the sensitivities of 90%, 80%, and 96%, and specificities of 86.2%, 93.8%, and 100% among untreated and MWA-treated cancerous tissue, and healthy lung tissue, respectively. These results indicate that Raman spectroscopy combined with multivariate analysis techniques can be used to explore the biochemical response mechanism of cancerous tissue to MWA therapy.

Fig. 1.

(a) The mean ± standard deviations (SD) of normalized spectra in cancerous tissue of the Pre-MWA group, Post-MWA group and healthy group, shading area represents standard deviations. (b) The differential spectra calculated from the mean Raman spectra between the cancerous tissue of Pre-MWA group and Post-MWA groups.

Fig. 2.

Scatter plots of the diagnostically significantly principal components scores for the three types of tissue, (a) PC1 versus PC2; (b) PC1 versus PC3; and (c) PC2 versus PC3.

Fig. 3.

The PCA loading of PC1, PC2 and PC3.

Fig. 4.

(a) represents the scatter plot of linear discriminant scores for three types of tissue; (b) displays two-dimensional ternary plot of the posterior probabilities belonging to the Pre-MWA group, Post-MWA group and healthy lung tissue calculated from the Raman spectral data set, using the PCA-LDA discriminant model combined with LOOCV method.

Fig. 5.

The ROC curves of discrimination results for the Raman spectral classification of Pre-MWA group, Post-MWA group and healthy lung tissue based on PCA-LDA algorithms together with the LOOCV method. AUC: the areas under the ROC curves.