Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

We investigate the potential of Raman microspectroscopy RMS for automated evaluation of excised skin tissue during Mohs micrographic surgery MMS. The main aim is to develop an auto- mated method for imaging and diagnosis of basal cell carcinoma BCC regions. Selected Raman bands responsible for the largest spec- tral differences between BCC and normal skin regions and linear dis- criminant analysis LDA are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra mea- sured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90±9% sensitivity and 85±9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an auto- mated objective method for tumor evaluation during MMS. The re- placement of current histopathology during MMS by a "generaliza- tion" of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need. ©