## 3 Results
### Raman Analysis  
Urothelial cell carcinomas were located and staged with OCT, where the loss of discriminatory structural features between the urothel to lamina propria, indicated pathological changes according to protocol shown in Figure 1. Raman spectra were acquired from these regions to determine the grade of the urothelial cell carcinoma. Raman signal was generated from biological macromolecules, such as mRNA, DNA, proteins, and lipids. The data were pre-processed as outlined in the Materials and Methods section, normalized to the C-H stretching band position at 2950 cm\({}^{-1}\). The RS feature vector covered the wavenumbers range from 300-3200 cm\({}^{-1}\) (1024 pixels, Newton 920i, Andor Technology). In Figure 5a, the median of low-grade and high-grade Raman spectra are shown. All Raman spectra were decomposed using principal component loading vectors, as shown in Figure 5b, and describe the significant differences between low-grade and high-grade Raman spectra. The following Raman bands were assigned as significant for distinguishing between low-grade and high-grade Raman spectra: 750 cm\({}^{-1}\), 853 cm\({}^{-1}\), 940 cm\({}^{-1}\), 1003 cm\({}^{-1}\), 1081 cm\({}^{-1}\), 1253 cm\({}^{-1}\), 1315 cm\({}^{-1}\), 1342 cm\({}^{-1}\), 1450 cm\({}^{-1}\), 1660 cm\({}^{-1}\). The assignments of the Raman bands are explained in the discussion. On average 20 Raman spectra were collected per biopsy and analyzed with PCA, where eight PCs explained 95% of the data variance and were used for further analysis. The variance captured by the first three PCs were: 64%, 12% and 8%, respectively. The first PC, which accounts for 64% of the variance, explains the differences in the Raman bands at 750 cm\({}^{-1}\), 853 cm\({}^{-1}\), 940 cm\({}^{-1}\), 1081 cm\({}^{-1}\), 1253 cm\({}^{-1}\), 1315 cm\({}^{-1}\), 1342 cm\({}^{-1}\), 1450 cm\({}^{-1}\) and 1660 cm\({}^{-1}\). The peaks at 1450 cm\({}^{-1}\) and 1660 cm\({}^{-1}\) are significant in PC 2 and PC 3, respectively.  
Figure 4: OCT analysis. (**a**) Median of pTa, pT1 and pT2 feature vectors of pathologic bladder tissue. (**b**) The first three loading vectors calculated by PCA. pTa, pT1 and pT2 tumors can be discriminated based on the differences in intensity of the peaks in the loadings of the principal components (PCs).  
The score values for PC 1 and PC 2 are shown in a scatter plot in Figure 6a, as well as for PC 2 and PC 3 in Figure 6b. The ellipses in Figure 6a are more distinct compared to Figure 6b, indicating that the discrimination performance of PC 1 vs. PC 2 is better than PC 2 vs. PC 3. The training of the kNN classifier with the PCs yielded an overall accuracy of 93%, see Table 1. To predict between low-grade and high-grade, sensitivity and specificity yielded 99% and 87%, respectively. Sensitivity corresponds to the likelihood of correctly identified high-grade tumor, whereas specificity indicates the chances of correctly predicted low-grade lesions.  
Figure 5: Raman analysis. (**a**) Median of low-grade and high-grade Raman spectra of pathologic bladder tissue. (**b**) The first three loading vectors calculated by PCA. Low-grade and high-grade tumors can be discriminated based on the differences in intensity of the peaks in the loadings of the principal components.  
Figure 6: Raman analysis. (**a**) Scores of the PCA plotted along the first two PCs and (**b**) along the second and the third PC. Circles show the discrimination of low-grade and high-grade tumors.