Improving Sensitivity and Specificity in Breast Cancer Detection Using Raman Spectroscopy and Bayesian Classification

Abstract

Automated real-time tissue assessment using Raman spectroscopy for breast cancer detection is feasible; however, long-term specificity and sensitivity as reported so far in the literature can still be improved by a more reliable algorithm for breast cancer detection. Applying automatic reduction of background fluorescence to the Raman spectra and Bayesian classification on 18 discriminant Raman bands in the range of 1200–1800 cm−1, we achieved 100% accuracy in classifying breast biopsies of healthy and cancerous tissues, making it suitable for automated breast cancer diagnosis and appropriate for long-term use in real time in a surgery room or research scenarios. The long-term reliability of this approach was cross-validated using three methods: resubstitution, leave-one-out, and holdout. The holdout method has the potential of estimating the upper bound of classification error probability of the Bayesian classifier; the holdout method allowed us to perform 50,000 classification trials with only three misclassifications, which demonstrates that the high performance in sensitivity and specificity will be retained in future applications of this approach for breast cancer detection.