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Histological Classification of Raman Spectra of Human Coronary Artery Atherosclerosis Using Principal Component Analysis
Volume 53, Number 8 (Aug. 1999) Page 938-942
Deinum, Geurt; Rodriguez, Daniel; Romer, Tjeerd J.; Fitzmaurice, Maryann; Kramer, John R.; Feld, Michael S.
We present a nonparametric method of analysis of Raman spectra of coronary artery tissue to classify atherosclerotic lesions. The method correlates the principal component scores of the Raman spectra with the tissue pathology. A data set composed of 97 samples of human coronary artery was used to develop the diagnostic algorithm, and a second data set composed of 68 samples was then used to test this algorithm prospectively. The results show that the algorithm can accurately classify coronary artery tissue into three classes: nonatherosclerotic, noncalcified plaque, and calcified plaque. The accuracy of this classification scheme is comparable to that previously achieved by means of a biochemical analysis of the Raman spectra using the same data.