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Page 10 of 14                                Liu et al. J Cancer Metastasis Treat 2019;5:4  I  http://dx.doi.org/10.20517/2394-4722.2018.55



















               Figure 6. (left) is a scatter plot of scores PC1 and PC3 with the separation line (SVM classifier); (middle) ROC curve for the SVM
               classifier in the left panel; (right) ROC curve for the SVM classifier trained with PC1, PC3 and PC7. PC: principal component; ROC: receiver
               operating characteristic; SVM: supports vector machine

               BCC tissue samples. These ratios of the intensities of the spectral peaks from the BCC cancer tissue at depth
               100 µm are found to be much higher [Figure 5 (left and middle)] or lower [Figure 5 (right)] in comparison
               with those from normal skin tissue, therefore they may be used as a distinct marker to distinguish cancerous
               tissues from normal skin tissues. Such ratios provide an insight into the conformational changes occurring
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               in biomolecules in these tissues. For instance, the Raman spectra of proteins (represented by the 1,662 cm
               band) correspond to beta-sheets which provide insight into protein folding or the denaturation processes.

               Classification based on RR spectral data of BCC and normal skin tissues by SVM
               The spectral dataset including 12 spectra from sliced normal skin tissues, and 43 spectra from sliced BCC
               cancerous samples were analyzed using PCA. Fifty-five PCs were obtained with corresponding eigenvalues
               sorted in a descent order (the plots are not shown here). The first 10 PCs account for 97% of the total variance.
               If two PCs are selected for classification, first PC (PC1) and third PC (PC3) showed the best performance.
               The scatter plot of the PC scores along with a boundary line trained using SVM method is shown in Figure 6
               (left). The sensitivity, specificity and accuracy of the classifier with re-substitution validation were calculated
               to be 97.7%, 75.0%, and 92.7%, respectively. The ROC curve was generated and shown in Figure 6 (middle).
               The AUROC was found to be 0.95. When PC1, PC3 and PC7 are used together for classification, it achieved
               optimal performance. Sensitivity, specificity and accuracy of the SVM classifier trained with all spectra
               were found to be 93.0%, 100%, and 94.5% with re-substitution validation. The ROC curve was generated and
               shown in Figure 6 (right). The AUROC was found to be 0.99. LOOCV achieved sensitivity 97.7%, specificity
               66.7%, and accuracy 90.9%. The classification is shown to be effective for the diagnosis of human skin tissues
               using RR spectroscopy.


               DISCUSSION
               In conclusion, given our preliminary investigation, we have demonstrated how the molecular components
               and conformation change under different conditions of BCC skin cancer tissues, and shown that there is a
               correlation between the depth dependence of RR spectra and the status change of BCC tissue at a molecular
               level. At a depth of 100 µm, the VRR spectra from BCC tissue change significantly compared to the spectra
               from normal skin tissues due to the changes in the relative concentrations of tryptophan, carotenoids, lipids
               and proteins [Figures 2-5]. In addition, VRR technique with 532 nm excitation can effectively distinguish
               BCC from normal skin tissues. The PCA-SVM statistical analyses of the VRR data collected from human
               skin cancer and normal tissues were used to distinguish BCC lesions from normal skin tissues. It yielded
               a sensitivity, specificity and accuracy of 93.0%, 100%, and 94.5%, respectively, when compared with the
               histopathology analysis (as the “gold standard”) reports. This is the first evidence that the difference between
               human skin normal tissues and cancer lesions can be detected by VRR spectroscopy.
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