Optimization of multimodal spectral imaging for assessment of resection margins during Mohs micrographic surgery for basal cell carcinoma

Abstract

Multimodal spectral imaging (MSI) based on auto-fluorescence imaging and Raman micro-spectroscopy was used to detect basal cell carcinoma (BCC) in tissue specimens excised during Mohs micrographic surgery. In this study, the MSI algorithm was optimized to maximize the diagnosis accuracy while minimizing the number of Raman spectra: the segmentation of the auto-fluorescence images was optimized according to the type of BCC, sampling points for Raman spectroscopy were generated based on auto-fluorescence intensity variance and segment area, additional Raman spectra were acquired when performance of the segmentation algorithm was sub-optimal. The results indicate that accurate diagnosis can be achieved with a sampling density of ~2,000 Raman spectra/cm 2 , based on sampling points generated by the MSI algorithms. The key benefit of MSI is that diagnosis of BCC is obtained based on intrinsic chemical contrast of the tissue, within time scales similar to frozen-section histopathology, but without requiring laborious sample preparation and subjective interpretation of stained frozen-sections.