Towards intra-operative diagnosis of tumours during breast conserving surgery by selective-sampling Raman micro-spectroscopy

Breast-conserving surgery (BCS) is increasingly employed for the treatment of early stage breast cancer. One of the key challenges in BCS is to ensure complete removal of the tumour while conserving as much healthy tissue as possible. In this study we have investigated the potential of Raman microspectroscopy (RMS) for automated intra-operative evaluation of tumour excision. First, a multivariate classification model based on Raman spectra of normal and malignant breast tissue samples was built and achieved diagnosis of mammary ductal carcinoma (DC) with 95.6% sensitivity and 96.2% specificity (5-fold cross-validation). The tumour regions were discriminated from the healthy tissue structures based on increased concentration of nucleic acids and reduced concentration of collagen and fat. The multivariate classification model was then applied to sections from fresh tissue of new patients to produce diagnosis images for DC. The diagnosis images obtained by raster scanning RMS were in agreement with the conventional histopathology diagnosis but were limited to long data acquisition times (typically 10 000 spectra mm(-2), which is equivalent to similar to 5 h mm(-2)). Selective-sampling based on integrated auto-fluorescence imaging and Raman spectroscopy was used to reduce the number of Raman spectra to similar to 20 spectra mm(-2), which is equivalent to an acquisition time of similar to 15 min for 5 x 5 mm(2) tissue samples. This study suggests that selective-sampling Raman microscopy has the potential to provide a rapid and objective intra-operative method to detect mammary carcinoma in tissue and assess resection margins.