We report on the development of a simultaneous fingerprint and high-wavenumber (FP/HW) Raman endoscopy platform for real-time, in vivo diagnosis of bladder cancer during transurethral resection of bladder tumor (TURBT). Significant tissue Raman spectral differences are observed in both FP (i.e., 800 – 1800 cm-1) and HW (i.e., 2800 – 3600 cm-1) regions between normal and cancer as well as between normal and carcinoma in situ (CIS) tissue sites, indicating the biomolecular differences among cancer, CIS and normal tissue sites. A cancer diagnosis model has been developed based on partial-least-squares linear-discriminant-analysis (PLS-LDA) with leave-one-tissue site-out cross-validation (LOOCV). The diagnosis model yields the diagnostic accuracy of > 90 % for identifying both cancer and CIS sites from normal bladder tissue. Through this work, we demonstrate that in vivo FP/HW fiberoptic Raman endoscopy is a promising and effective clinical tool for rapid diagnosis of cancer and pre-cancer tissue sites during TURBT from biomolecular level.
Current Hyperspectral stimulated Raman scattering (hsSRS) data analysis methods face challenges when it comes to rapidly and reliably quantifying different lipid subtypes, and cannot fully leverage the information in hsSRS data. Here, we present a rapid and reliable quantitative algorithm for quantitative analysis that fully extracts chemical information by using adaptive selection of Lorentzian basis functions to fit the spectra in hsSRS data in bulk. We demonstrated that, by utilizing the ratio relationships between fitted bands, quantitative comparisons of specific lipid subtypes can be achieved. Moreover, we applied our method for the quantitative analysis of lipid composition in lipid droplets based on hsSRS data of liver cancer tissues and confirmed our method has a better fitting effect and a faster solving speed compared to MCR. This suggests that our method has the potential for great utility in the quantitative analysis of hsSRS imaging data for biomedical specimens.
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