Label-free detection of brain tumors in a 9L gliosarcoma rat model using stimulated Raman scattering-spectroscopic optical coherence tomography

Significance: In neurosurgery, it is essential to differentiate between tumor and healthy brain regions to maximize tumor resection while minimizing damage to vital healthy brain tissue. However, conventional intraoperative imaging tools used to guide neurosurgery are often unable to distinguish tumor margins, particularly in infiltrative tumor regions and low-grade gliomas. Aim: The aim of this work is to assess the feasibility of a label-free molecular imaging tool called stimulated Raman scattering-spectroscopic optical coherence tomography (SRS-SOCT) to differentiate between healthy brain tissue and tumor based on (1) structural biomarkers derived from the decay rate of signals as a function of depth and (2) molecular biomarkers based on relative differences in lipid and protein composition extracted from the SRS signals. Approach: SRS-SOCT combines the molecular sensitivity of SRS (based on vibrational spectroscopy) with the spatial and spectral multiplexing capabilities of SOCT to enable fast, spatially and spectrally resolved molecular imaging. SRS-SOCT is applied to image a 9L gliosarcoma rat tumor model, a well-characterized model that recapitulates human high-grade gliomas, including high proliferative capability, high vascularization, and infiltration at the margin. Structural and biochemical signatures acquired from SRS-SOCT are extracted to identify healthy and tumor tissues. Results: Data show that SRS-SOCT provides light-scattering-based signatures that correlate with the presence of tumors, similar to conventional OCT. Further, nonlinear phase changes from the SRS interaction, as measured with SRS-SOCT, provide an additional measure to clearly separate tumor tissue from healthy brain regions. We also show that the nonlinear phase signals in SRS-SOCT provide a signal-to-noise advantage over the nonlinear amplitude signals for identifying tumors. Conclusions: SRS-SOCT can distinguish both spatial and spectral features that identify tumor regions in the 9L gliosarcoma rat model. This tool provides fast, label-free, nondestructive, and spatially resolved molecular information that, with future development, can potentially assist in identifying tumor margins in neurosurgery.