Near-Infrared Raman Spectroscopy of Human Coronary Arteries: Histopathological Classification Based on Mahalanobis Distance

OBJECTIVE: In this study, near-infrared Raman spectroscopy (NIRS) was used for evaluation of human atherosclerotic lesions using a simple algorithm based on discriminant analysis. The Mahalanobis distance was used to classify the clustered spectral features extracted from NIRS of a total of 111 arterial fragments of human coronary arteries. BACKGROUND DATA: Raman spectroscopy has been used for diagnosis of a variety of diseases. For real-time applications, it is important to have a simple algorithm that could perform fast data acquisition and analysis. The ultimate goal is to obtain a feasible diagnosis, which discriminates various atherosclerotic lesions with high sensitivities and specificities. MATERIALS AND METHODS: Non-atherosclerotic (NA) arteries, atherosclerotic plaques without calcification (NC), and atherosclerotic plaques with classification (C) were obtained and scanned with an NIR Raman spectrometer with 830-nm laser excitation. An algorithm based on the discriminant analysis using the Mahalanobis distance of the clustered spectral features was used for tissue classification into three categories: Na, NC, and C. RESULTS: Human coronary arteries exhibit different spectral signatures depending on different bio-chemicals present in each tissue type such as collagen, cholesterol, and calcium hydroxyapatite, respectively. It is shown that our algorithm has a maximum sensitivity and specificity of 85% and 89%, respectively, for the diagnosis of the NA tissue type, 85% and 89% for the NC tissue type, and 100% and 100% for the C tissue type. CONCLUSION: An algorithm (with a minimum of mathematical and computational requirements) based on the discriminant analysis of spectral features has been developed to classify atherosclerotic lesions with high sensitivities and specificities.