Study on bladder cancer tissues with raman spectroscopy

The scope of this research lies in diagnosis of bladder cancer through Raman spectra. The spectra of bladder cancer and normal bladder were measured by using laser confocal Raman micro-spectroscopy. Principal component analysis/support vector machines was applied to the spectral dataset to construct diagnostic algorithms, then to detect the accuracy of these algorithms to determine histological diagnosis by leave-one-out cross validation from its Raman spectrum. It was showed that the peak intensity of nucleic acid (782, 1583 cm^-1) in bladder cancer and protein (1061, 1295, 2849, 2881 cm^-1) in normal bladder increased significantly. Additionally, Principal component analysis (PCA) and support vector machines (SVM) provided an effective tool for differentiating the bladder cancer from normal bladder tissue. Excellent sensitivity (86.7%), specificity (87.5%), positive predictive value (92.9%), and negative predictive value (72.8%) for the diagnosis of bladder cancer were obtained by leave-one-out cross validation. It was concluded that Raman spectroscopy can be used to accurately identify bladder cancer in vitro, and it suggests the promising potential application of PCA/SVM-based Raman spectroscopy for the diagnosis of bladder cancer.