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1 July 2004 Diagnosis of atherosclerosis in human carotid artery by FT-Raman spectroscopy: Principal Components Analysis algorithm
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FT- Raman Spectroscopy (FT-Raman) could allow identification and evaluation of human atherosclerotic lesions. A Raman spectrum can provide biochemical information of arteries which can help identifying the disease status and evolution. In this study, it is shown the results of FT-Raman for identification of human carotid arteries in vitro. Fragments of human carotid arteries were analyzed using a FT-Raman spectrometer with a Nd:YAG laser at 1064nm operating at an excitation power of 300mW. Spectra were obtained with 250 scans and spectral resolution of 4 cm-1. Each collection time was approximately 8 min. A total of 75 carotid fragments were spectroscopically scanned and FT-Raman results were compared with histopathology. Principal Components Analysis (PCA) was used to model an algorithm for tissue classification into three categories: normal, atherosclerotic plaque without calcification and atherosclerotic plaque with calcification. Non-atherosclerotic (normal) artery, atherosclerotic plaque and calcified plaque exhibit different spectral signatures related to biochemicals presented in each tissue type, such as, bands of collagen and elastin (proteins), cholesterol and its esters and calcium hydroxyapatite and carbonate apatite respectively. Results show that there is 96% match between classifications based on PCA algorithm and histopathology. The diagnostic applied over all 75 samples had sensitivity and specificity of about 89% and 100%, respectively, for atherosclerotic plaque and 100% and 98% for calcified plaque.
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Grazielle Vilela Nogueira, Landulfo Silveira Jr., Airton Abrahao Martin, Renato Amaro Zangaro, Marcos Tadeu Pacheco, Maria Cristina Chavantes, Marcelo Zampieri, and Carlos Augusto G. Pasqualucci "Diagnosis of atherosclerosis in human carotid artery by FT-Raman spectroscopy: Principal Components Analysis algorithm", Proc. SPIE 5321, Biomedical Vibrational Spectroscopy and Biohazard Detection Technologies, (1 July 2004);

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