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

Author(s)

Bodanese, Benito ^[1] ; Silveira, Fabricio Luiz ^[2] ; Zangaro, Renato Amaro ^[2] ; Pacheco, Marcos Tadeu T. ^[2] ; Pasqualucci, Carlos Augusto ^[3] ; Silveira, Jr., Landulfo ^[2]

Affiliation

^[1] Univ Comunitaria Reg Chapeco UNOCHAPECO, Hlth Sci Ctr CCS, Chapeco - Brazil

^[2] Univ Camilo Castelo Branco UNICASTELO, Nucleo Parque Tecnol Sao Jose dos Campos, Inst Biomed Engn, BR-12247004 Sao Jose Dos Campos, SP - Brazil

^[3] Univ Sao Paulo, Fac Med, Dept Cardiovasc Pathol, Sao Paulo - Brazil

• Document type: Journal article
• Source: Photomedicine and Laser Surgery; v. 30, n. 7, p. 381-387, JUL 2012.
• Web of Science Citations: 31

Abstract

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 similar to 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. (AU)

• FAPESP's process: 09/01788-5 - Dispersive Raman spectroscopy using optical fibers "Raman probe" applied to the diagnosis of neoplasia in the skin and prostate
• Grantee: Landulfo Silveira Junior
• Support Opportunities: Regular Research Grants