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• W2014768471 abstract "Raman spectroscopy has been used to detect spectral differences between normal and basocellular cell carcinoma (BCC) skin tissues that are related to biochemical alterations between tissues.Raman spectroscopy is an analytic tool that could detect biochemical alterations in tissues, and its use would lead to real-time and less-invasive cancer diagnosis.Raman spectra from human tissue fragments (normal and BCC) were obtained in a dispersive, near-infrared Raman spectrometer (laser parameters: 830 nm, 80 mW) with a CCD detector. Spectral changes between normal and BCC were analyzed with a principal components analysis (PCA) algorithm and a simplified biochemical model based on the relative amount of collagen and cell fat extracted from tissue Raman spectra.Main spectral differences between these samples were in the region of 800 to 1,000 per centimeter and 1,200 to 1,300 per centimeter, corresponding to vibrational bands from lipids and proteins (C-C bonds and amide III, respectively). The diagnostic algorithm based on PCA and Mahalanobis distance applied to the scores of principal components vectors PC1 and PC2 could identify tissue with sensitivity and specificity of 89% and 93%, respectively, for the training group and 96% and 92% for the prospective group. The simplified biochemical model for collagen amount had sensitivity and specificity of 95% and 83% for the training group and 87% and 92% for the prospective group.Raman spectroscopy could differentiate between normal and BCC tissues in both the PCA and biochemical models, showing higher sensitivity and specificity for the PCA model, although the simplified biochemical model is easier to implement." @default.
• W2014768471 created "2016-06-24" @default.
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• W2014768471 date "2010-08-01" @default.
• W2014768471 modified "2023-09-26" @default.
• W2014768471 title "Differentiating Normal and Basal Cell Carcinoma Human Skin Tissues <i>In Vitro</i> Using Dispersive Raman Spectroscopy: A Comparison Between Principal Components Analysis and Simplified Biochemical Models" @default.
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• W2014768471 doi "https://doi.org/10.1089/pho.2009.2565" @default.
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