## 3 Results
### OCT Analysis  
The goal of OCT investigation comprised the discrimination between healthy and pathologic tissue and determination of the position and borders of the suspicious lesions via live viewing OCT of 20 B-scans per second, including real-time processing allowing for navigation and screening without noticeable time lag. Additionally, OCT allowed for the estimation of penetration depth of the lesion, as well as identification of structural features leading to the staging of the urothelial carcinoma. In Figure 2 a cross-section of a healthy bladder wall is shown where urothel (U), lamina propria (LP), and muscularis (M) could clearly be identified. The urothel penetrates approximately 50 \(\upmu\)m into the tissue, followed by the lamina propria up to \(\sim\)150 \(\upmu\)m, and muscularis, which starts at a depth of \(\sim\)150-200 \(\upmu\)m, depending on the dimensions of urothel and lamina propria. These values can differ as urothel and lamina propria vary depending on the site in the bladder. In healthy bladder tissue urothel, lamina propria and muscularis were well separated, due to different scattering coefficients. Lamina propria exhibited high scattering intensity levels and therefore was easily identifiable. In addition, structural features, such as vessels (V) could be identified (see arrow). Figure 3 demonstrates the loss of structural features with progressive degeneration of healthy bladder wall. As the structural features of urothel, lamina propria and their separation were lost, OCT determined the stage of the tumor based on the conserved features, such as small vessels (see arrows Figure 3a). T1 staged tumor (see Figure 3a) is a non-muscle invasive bladder cancer, which progresses until the lamina propria, hence the structural information of the urothel is lost and only a few features in the lamina propria are conserved. T2 staged tumor (see Figure 3b) invades the muscularis and thereby destroys all structural features, leaving an OCT image without any visible information content. Borders between urothel and lamina propria and, furthermore, muscularis are not identifiable (see Figure 3b). An empirical approach to data analysis via visual inspection was chosen. Firstly, tomograms were divided in healthy and pathologic bladder images based on OCT pre-screening. In all pathologic tissue the clear layer discrimination of the healthy tissue is faded/washed out extensively. Secondly, only tomograms showing malignant tissue were acquired and used for further investigations. OCT volumes were only acquired for samples that were large enough to cover the entire FOV (1.2 \(\times\) 1.2 mm) as the texture analysis needed the input of the same field size. Texture analysis of these pathologic OCT data achieved a sensitivity and specificity of 80% and 60%, respectively. In this case, sensitivity describes the correct identification of tumors staged pT2, whereas specificity explained the likelihood of correct identification of pTa staged lesions. The overall accuracy yielded 71%, see Table 1.  
Figure 2: OCT image of a healthy bladder wall with urothel (U), intact lamina propria (LP) and muscularis (M) clearly distinguishable by their refraction indices. Vessels (V) are located in the LP. Figure 4a describes the medians of the three different stages diagnosed with OCT, namely pTa, pT1 and pT2. A clear difference between the stages can be determined based on the texture feature intensities of the stages as with progressed stage the intensity levels rise. The OCT feature vector covers the textural features in four different angles (0, 45, 90, 135) yielding 16 features at first. To avoid the influence of corrupted B-Scans within one OCT volume batch, we further computed the mean, maximum, standard deviation, median and minimum of all 16 features to generate in total 80 features. The first 16 entries of the texture feature vector comprise the mean from all the B-scans in one batch for feature contrast, correlation, energy and homogeneity at the four different angles.