Diagnosis of Tumors During Tissue-conserving Surgery with Integrated Autofluorescence and Raman Scattering Microscopy

Tissue-conserving surgery is used increasingly in cancer treatment. However, one of the main challenges in this type of surgery is the detection of tumor margins. Histopathology based on tissue sectioning and staining has been the gold standard for cancer diagnosis for more than a century. However, its use during tissue-conserving surgery is limited by time-consuming tissue preparation steps (1-2 h) and the diagnostic variability inherent in subjective image interpretation. Here, we demonstrate an integrated optical technique based on tissue autofluorescence imaging (high sensitivity and high speed but low specificity) and Raman scattering (high sensitivity and high specificity but low speed) that can overcome these limitations. Automated segmentation of autofluorescence images was used to select and prioritize the sampling points for Raman spectroscopy, which then was used to establish the diagnosis based on a spectral classification model (100% sensitivity, 92% specificity per spectrum). This automated sampling strategy allowed objective diagnosis of basal cell carcinoma in skin tissue samples excised during Mohs micrographic surgery faster than frozen section histopathology, and one or two orders of magnitude faster than previous techniques based on infrared or Raman microscopy. We also show that this technique can diagnose the presence or absence of tumors in unsectioned tissue layers, thus eliminating the need for tissue sectioning. This study demonstrates the potential of this technique to provide a rapid and objective intraoperative method to spare healthy tissue and reduce unnecessary surgery by determining whether tumor cells have been removed.