Abstract
.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.
Highlights
Resection is the first line therapy to manage brain tumors, where the survival rate of patients increases with the extent of resection.[1,2,3] it is critically important to minimize removal of healthy brain tissue to avoid deficits in brain function, which could have severe consequences for patients
3.1 Processing stimulated Raman scattering (SRS)-SOCT Dataset To extract the complex nonlinear SRS signals, the interferometric spectra first are interpolated from wavelength to a linear wavenumber array (k 1⁄4 2π∕λ) and Fourier transformed from
We have shown that SRS-SOCT has the capability of yielding distinguishable features of brain tumor and healthy tissues in both the spectral and spatial domains, using a 9L gliosarcoma rat tumor model
Summary
Resection is the first line therapy to manage brain tumors, where the survival rate of patients increases with the extent of resection.[1,2,3] it is critically important to minimize removal of healthy brain tissue to avoid deficits in brain function, which could have severe consequences for patients. Optical coherence tomography (OCT), for instance, has been used to guide brain cancer surgery by leveraging the optical attenuation difference between tumor and benign tissues.[12] Another example is stimulated Raman scattering (SRS), which measures the intrinsic vibrational properties of molecules.[13] Previous studies have shown that lipid and protein contents, as measured with SRS, can differentiate between healthy and tumor brain tissues.[14,15,16] SRS has been successfully applied to guide neurosurgery but only using excised tissues,[17,18,19,20,21] and its implementation for in vivo intraoperative image guidance has been challenging.[17,22,23,24] Further, unlike OCT, SRS is a point-scanning method and does not offer the convenient spatial multiplexing capability that renders OCT a fast volumetric imaging tool. The unique molecular information available with SRS— from proteins and lipids, which have a high Raman cross section—makes it an attractive candidate to identify tumor margins based on biochemical contrast
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