Comparison of autofluorescence, diffuse reflectance, and Raman spectroscopy for breast tissue discrimination

For a given diagnostic problem, important considerations are the relative performances of the various optical biopsy techniques. A comparative evaluation of fluorescence, diffuse reflectance, combined fluorescence and diffuse reflectance, and Raman spectroscopy in discriminating different histopathologic categories of human breast tissues is reported. Optical spectra were acquired ex vivo from a total of 74 breast tissue samples belonging to 4 distinct histopathologic categories: invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS), fibroadenoma (FA), and normal breast tissue. A probability-based multivariate statistical algorithm capable of direct multiclass classification was developed to analyze the diagnostic content of the spectra measured from the same set of breast tissue sites with these different techniques. The algorithm uses the theory of nonlinear maximum representation and discrimination feature for feature extraction, and the theory of sparse multinomial logistic regression for classification. The results reveal that the performance of Raman spectroscopy is superior to that of all others in classifying the breast tissues into respective histopathologic categories. The best classification accuracy was observed to be approximately 99%, 94%, 98%, and 100% for IDC, DCIS, FA, and normal breast tissues, respectively, on the basis of leave-one-sample-out cross-validation, with an overall accuracy of approximately 99%.