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

Bodanese, Benito; Silveira, Landulfo; Albertini, Regiane; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu Tavares

doi:10.1089/pho.2009.2565

Cited by 48 publications

(54 citation statements)

References 36 publications

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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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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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“…12,17 The spectral differences of non-melanoma and MEL lesions in skin tissues compared with normal ones (N) have been studied with Raman spectroscopy. [18][19][20][21][22][23][24][25][26] The discrimination of MEL and/or basal cell carcinoma (BCC) from N in vitro and in vivo has been achieved through algorithms implemented using statistical 18,23,25,27 and biochemical [22][23][24]27,28 methods. Statistical methods include principal component analysis (PCA), which is a data reduction technique that can be used to group spectra with selected features according to differences in the pathology, through a suitable discrimination technique (such as linear discriminant analysis and Euclidean or Mahalanobis distances) applied to the most relevant principal components.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Basal Cell Carcinoma and Melanoma from Normal Skin Biopsies in Vitro Through Raman Spectroscopy and Principal Component Analysis

Bodanese

Silveira

Zângaro

et al. 2012

Photomedicine and Laser Surgery

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Objective: Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. Background data: Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively. Methods: Skin biopsy fragments of *2 mm 2 from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues. Results: We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p < 0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%. Conclusions: This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.

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“…By measuring changes in the wavelength of light scattered from a sample, the biochemical makeup of the tissue may be inferred. In conjunction with multivariate chemometric methodologies that analyze the spectral information available over all the spectral channels [12], Raman spectroscopy has been shown to provide clinically relevant diagnostic information in a wide variety of disease types ranging from atherosclerosis to breast and skin cancer [13][14][15][16][17]. Minimally invasive endoscopic techniques aimed at detection of cancer cells in the lining of the urinary bladder using biophotonic technology such as Raman spectroscopy can interrogate the molecular composition of the cells and provide an objective and highly specific picture of the pathology.…”

Section: Introductionmentioning

confidence: 99%

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

et al. 2012

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In recent years, Raman spectroscopy has shown substantive promise in diagnosing bladder cancer, especially due to its exquisite molecular specificity. The ability to reduce false detection rates in comparison to existing diagnostic tools such as photodynamic diagnosis makes Raman spectroscopy particularly attractive as a complementary diagnostic tool for real-time guidance of transurethral resection of bladder tumor (TURBT). Nevertheless, the state-ofthe-art high-volume Raman spectroscopic probes have not reached the expected levels of specificity thereby impeding their clinical translation. To address this issue, we propose the use of a confocal Raman probe for bladder cancer diagnosis that can boost the specificity of the diagnostic algorithm based on its suppression of the out-of-focus nonanalyte-specific signals emanating from the neighboring normal tissue. In this article, we engineer and apply such a probe, having depth of field of approximately 280 μm, for Raman spectral acquisition from ex vivo normal and cancerous TURBT samples. Using this clinical dataset, a diagnostic algorithm based on principal component analysis and logistic regression is developed. We demonstrate that this approach results in comparable sensitivity but significantly higher specificity in relation to high-volume Raman spectral data. The application of only two principal components is sufficient for the discrimination of the samples underlining the robustness of the algorithm. Further, no discordance between replicate spectra is observed emphasizing the reproducible nature of the current diagnostic assessment. The high levels of sensitivity and specificity achieved in this proof-of-concept study opens substantive avenues for application of a confocal Raman probe during endoscopic procedures related to diagnosis and treatment of bladder cancer.

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Differentiating Normal and Basal Cell Carcinoma Human Skin Tissues In Vitro Using Dispersive Raman Spectroscopy: A Comparison Between Principal Components Analysis and Simplified Biochemical Models

Abstract: 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.

Cited by 48 publications

References 36 publications

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

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

Discrimination of Basal Cell Carcinoma and Melanoma from Normal Skin Biopsies in Vitro Through Raman Spectroscopy and Principal Component Analysis

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

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