Diagnosing urothelial carcinoma through multiple spectroscopic techniques

Urothelial carcinoma (UC) is the most common type of bladder cancer, and its treatment depends from both tumour invasiveness (stage) and aggressiveness (grade). The gold standard for detecting UC is white-light cystoscopy, followed by tissue biopsy and histopathological examination; however, such process is invasive, time-consuming, operatordependent and prone to sampling errors. In this framework, optical spectroscopy techniques can provide a fast, label-free and non-invasive tool for improving diagnosis. Thus, we combined auto-fluorescence, diffuse reflectance and Raman spectroscopy in a compact and transportable setup based on an optical fibre-bundle probe. This experimental setup was used for studying fresh biopsies of urothelial tumour (129 samples) and healthy bladder (40 samples) collected from 78 patients undergoing Transurethral Resection of Bladder Tumours (TURBT). The recorded data were analysed using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for obtaining an automated classification of the examined samples based on the intrinsic spectral information provided by all three techniques. We found that healthy and diseased tissues showed significant spectral differences for each technique, resulting in high accuracy (up to 90%) from PCA-LDA routines. While fluorescence spectroscopy seems sensitive enough for detecting UC, we found that a multimodal approach is crucial for obtaining high discriminating capability (<80%) in grading and staging tumour biopsies. In conclusion, the presented strategy generates results similar to gold standard histology, but in a fast and labelfree way, offering the potential for endoscopic in vivo applications.