Discrimination of non‐melanoma skin lesions from non‐tumor human skin tissues <i>in vivo</i> using Raman spectroscopy and multivariate statistics

Silveira, Fabrício L.; Pacheco, Marcos Tadeu Tavares; Bodanese, Benito; Pasqualucci, Carlos Augusto; Zângaro, Renato Amaro; Silveira, Landulfo

doi:10.1002/lsm.22318

Cited by 38 publications

(36 citation statements)

References 48 publications

(182 reference statements)

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“…To develop RS methods for cancer diagnostics, various scientific groups use highly sensitive equipment with a high degree of detector cooling . As we can see from the considered works, basically, the cooling range from −120°C to −60°C has been used.…”

Section: Discussionmentioning

confidence: 99%

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

Khristoforova

Bratchenko

Myakinin

et al. 2019

Journal of Biophotonics

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The present paper studies the applicability of a portable cost‐effective spectroscopic system for the optical screening of skin tumors. in vivo studies of Raman scattering and autofluorescence (AF) of skin tumors with the 785 nm excitation laser in the near‐infrared region included malignant melanoma, basal cell carcinoma and various types of benign neoplasms. The efficiency of the portable system was evaluated by comparison with a highly sensitive spectroscopic system and with the diagnosis accuracy of a human oncologist. Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78.9% to 100%. Hundred percent accuracy of benign and malignant skin tumors differentiation is possible only with a combined analysis of Raman and AF signals.

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Section: Discussionmentioning

confidence: 99%

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

Khristoforova

Bratchenko

Myakinin

et al. 2019

Journal of Biophotonics

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“…Raman spectral features extracted from PCA have been used to reveal the differences in the biochemistry of normal and altered/pathological status of tissues and fluids, thus being able to discriminate histological groups of skin cancer in vitro and in vivo (Silveira et al, 2015) and atherosclerosis in coronary arteries (Silveira et al, 2002), differential diagnosis in uveitis and endophthalmitis (Rossi et al, 2012), and detecting biomarkers for diseases in biological fluids such as serum and urine (Bispo et al, 2013;Saade et al, 2008). The loading vectors may be an important tool to show the differences in the biochemistry when the spectra of pure biochemicals cannot be obtained, and may present the same results in discrimination models using spectra of standard biochemicals (Bodanese et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Detecting alterations of glucose and lipid components in human serum by near-infrared Raman spectroscopy

et al. 2015

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Introduction: Raman spectroscopy may become a tool for the analysis of glucose and triglycerides in human serum in real time. This study aimed to detect spectral differences in lipid and glucose components of human serum, thus evaluating the feasibility of Raman spectroscopy for diagnostic purposes. Methods: A total of 44 samples of blood serum were collected from volunteers and submitted for clinical blood biochemical analysis. The concentrations of glucose, cholesterol, triglycerides, and low-density and high-density lipoproteins (LDL and HDL) were obtained using standard biochemical assays. Serum samples were placed in Eppendorf tubes (200 µL), kept cooled (5 °C) and analyzed with near-infrared Raman spectroscopy (830 nm, 250 mW, 50 s accumulation). The mean spectra of serum with normal or altered concentrations of each parameter were compared to determine which Raman bands were related to the differences between these two groups.Results: Differences in peak intensities of altered sera compared to normal ones depended on the parameter under analysis: for glucose, peaks were related to glucose; for lipid compounds the main changes occurred in the peaks related to cholesterol, lipids (mainly triolein) and proteins. Principal Components Analysis discriminated altered glucose, cholesterol and triglycerides from the normal serum based on the differences in the concentration of these compounds. Conclusion: Differences in the peak intensities of selected Raman bands could be seen in normal and altered blood serum samples, and may be employed as a means of diagnosis in clinical analysis.

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“…The PCA extracts the most significant information (based on the variance) from an original dataset, generating two new variables, called principal components loading vectors (PCs) and scores (SCs), where each PC loading vector, which resemble Raman spectra, presents a "weight," the SC, which indicates the intensity of each loading that is present in the original data. 29,32,34,35 From these two variables, the similarities and differences in the groups can be identified. The largest spectral variation is stored in PC1, and the extraction of the variations follows successively (PC2, PC3, etc.)…”

Section: Exploratory Analysis By Principal Component Analysismentioning

confidence: 99%

“…The use of multivariate analysis with the objective of identifying and classifying sample groups in the most diverse areas of knowledge is well known. 24,34,35,37,42,51 The PCA aims to perform the segregation of groups based on the variances that, maximized, support the classification of samples according to the differences between the groups, and has shown to be an important tool when the main information capable of differentiating the sample groups is just the variability between groups, which needs to be greater than the intragroup variability. 38,39 However, the PLS-DA stands out from the PCA-DA because, in addition to the information of the differences between the groups, the variances obtained within each group are also recognized, and these variances are associated to the groups when modeling the regression curve; [38][39][40] therefore, the PLS-DA leads to better performance in the classification of samples when compared to PCA-DA.…”

Section: Discriminant Analysis (Pls-da and Pca-da)mentioning

confidence: 99%

Spectral model for diagnosis of acute leukemias in whole blood and plasma through Raman spectroscopy

et al. 2018

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Acute leukemias are oncohematological diseases that compromise the bone marrow and have a complex diagnostic definition, leading to a high mortality when diagnosed late. This study proposed to determine the spectral differences between whole blood and plasma samples of healthy and leukemic subjects based on Raman spectroscopy (RS), correlating these differences with their resulting biochemical alterations and performing discriminant analysis of the samples (n ¼ 38 whole blood and n ¼ 40 plasma samples). Raman spectra were obtained using a dispersive Raman spectrometer (830-nm wavelength, 280-mW laser power, 30-s exposure time) with a Raman probe. The exploratory analysis based on principal component analysis (PCA) of the blood and plasma sample's spectra showed loading vectors with peaks related to amino acids, proteins, carbohydrates, lipids, and carotenoids, being the spectral differences related to amino acids and proteins for whole blood samples, and mainly carotenoids for plasma samples. Discriminant models based on partial least squares (PLS) and PCA were developed and classified the spectra as healthy or leukemic, with sensitivity of 91.9% (PLS) and 83.9% (PCA), specificity of 100% (both PLS and PCA), and overall accuracy of 96.5% (PLS) and 93.0% (PCA) for the whole blood spectra. In plasma, the sensitivity was 95.7% (PLS) and 11.6% (PCA), specificity of 98% (PLS) and 100% (PCA), and overall accuracy of 97.1% (PLS) and 64.1% (PCA). The study demonstrated that RS is a technique with potential to be applied in the diagnosis of acute leukemias in whole blood samples.

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Discrimination of non‐melanoma skin lesions from non‐tumor human skin tissues in vivo using Raman spectroscopy and multivariate statistics

Abstract: PCA and PLS could discriminate Raman spectra of skin tissues, opening the way for an in vivo optical diagnosis.

Cited by 38 publications

References 48 publications

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

Detecting alterations of glucose and lipid components in human serum by near-infrared Raman spectroscopy

Spectral model for diagnosis of acute leukemias in whole blood and plasma through Raman spectroscopy

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