## 4 Discussion
PC 1 explains differences at 750 cm\({}^{-1}\) (Symmetric breathing of tryptophan), 853 cm\({}^{-1}\) (ring breathing of tyrosine), 940 cm\({}^{-1}\) (C-C stretching \begin{table}
\begin{tabular}{c c c c} \hline  & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline OCT staging pathologic tissue & 80\% & 60\% & 71\% \\ Raman grading pathologic tissue & 99\% & 87\% & 93\% \\ \hline \end{tabular}
\end{table} 
Table 1: Diagnostic accuracy of OCT staging the suspicious lesion and RS grading. After visual inspection, the analysis algorithms were only applied to pathologic OCT images and Raman spectra. Sensitivity describes the likelihood of detecting pT2 for OCT or high-grade tumor in case of RS, whereas specificity explains the correct detection of pTa or low-grade tumor for OCT and RS, respectively. in protein), 1081 cm\({}^{-1}\) (C-C and C-O in lipids and glucose, C-N stretching of proteins), 1253 cm\({}^{-1}\) (amide III, C-N stretching mode of proteins, collagen), 1315 cm\({}^{-1}\) (CH\({}_{3}\)CH\({}_{2}\) twisting mode of lipids and collagen), 1342 cm\({}^{-1}\) (DNA/RNA and C-H deformation of proteins and tryptophan), 1450 cm\({}^{-1}\) (CH\({}_{2}\) bending of lipids and proteins in general), 1660 cm\({}^{-1}\) (amide I, proteins, C=C stretching of lipids). PC 2 and PC 3 have prominent peaks at 1450 cm\({}^{-1}\) (CH\({}_{2}\) bending of lipids and proteins in general), 1660 cm\({}^{-1}\) (amide I, proteins, C=C stretching of lipids). The scatter plot of the two principal components evaluated via PCA shows the separability of low-grade and high-grade Raman spectra (Figure 6a,b). With this result, a classifier was trained to automate the grading of the Raman spectra. A fine kNN-classifier yielded a sensitivity of 99%, specificity of 87% and overall accuracy of 93%. The classifier was only trained on low-grade and high-grade tumors. As our data is limited to eight patients and we only aim for a proof of principle, tests on the specificity, sensitivity and accuracy are representative within error margins of 2% for RS (specificity: 2%, sensitivity: 2%, accuracy: 2%) and 7-10% for OCT (specificity: 7%, sensitivity: 10%, accuracy: 9%). As previously explained for OCT data stability tests, we also included a false data set for the data stability test for RS. The difference in performance loss can be explained as less OCT data than RS data were used for analysis. Thereby further studies need to validate our approach for routine clinical use. Nonetheless, our approach hints towards the capability of RS to assist surgeons in providing grading of suspicious lesions in a matter of seconds. OCT in combination with RS allows for the diagnosis of bladder cancer regarding its stage and grade. As OCT offers the identification and analysis of structural features to discriminate healthy tissue from cancerous lesions with high speed and high resolution, RS complements this information with molecular characteristics to grade the tissue of interest. Until now, OCT and RS have been separately demonstrated to achieve reasonable sensitivity, specificity and overall accuracy [15; 31]. The presented study showed the feasibility of combining the two modalities to achieve faster diagnostic. As the presented study did not implement co-registration, future work will be directed toward a combined OCT-RS probe. Another further perspective is the combination of the two modalities in one endoscope, as already demonstrated for OCT and Raman separately [15; 29]. Thereby, the number of resected biopsies could be reduced, and the grading of the tumor as a diagnostic finding would be delivered immediately by a trained classifier or neural network. Additionally, as OCT is capable of detecting a small lesion, more tumors will be detected earlier and resected for a better outcome for the patient.