Melanoma Diagnosis by Raman Spectroscopy and Neural Networks: Structure Alterations in Proteins and Lipids in Intact Cancer Tissue

Abstract

Melanoma is the most aggressive skin cancer. The specificity and sensitivity of clinical diagnosis varies from around 40% to 80%. Here, we investigated whether the chemical changes in the melanoma tissue detected by Raman spectroscopy and neural networks can be used for diagnostic purposes. Near-infrared Fourier transform Raman spectra were obtained from samples of melanoma (n=22) and other skin tumors that can be clinically confused with melanoma: pigmented nevi (n=41), basal cell carcinoma (n=48), seborrheic keratoses (n=23), and normal skin (n=89). A sensitivity analysis of spectral frequencies used by a neural network was performed to determine the importance of the individual components in the Raman spectra. Visual inspection of the Raman spectra suggested that melanoma could be differentiated from pigmented nevi, basal cell carcinoma, seborrheic keratoses, and normal skin due to the decrease in the intensity of the amide I protein band around 1660 cm-1. Moreover, melanoma and basal cell carcinoma showed an increase in the intensity of the lipid-specific band peaks around 1310 cm-1 and 1330 cm-1, respectively. Band alterations used in the visual inspection were also independently identified by a neural network for melanoma diagnosis. The sensitivity and specificity for diagnosis of melanoma achieved by neural network analysis of Raman spectra were 85% and 99%, respectively. We propose that neural network analysis of near-infrared Fourier transform Raman spectra could provide a novel method for rapid, automated skin cancer diagnosis on unstained skin samples.

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

“…Other studies have also shown the ability of Raman spectroscopy to accurately classify diseased tissue in brain, 16,17 skin, [18][19][20][21][22][23][24] cervix, [25][26][27] breast, 1,2,9,10,28-33 and other organs. 8,11,[34][35][36][37][38][39][40][41][42][43][44] Our data is confirmatory, and, in addition, demonstrates changes in the tumor bed, which probably represent the detection of chemical signs of preneoplastic changes.…”

Differential diagnosis in primary and metastatic cutaneous melanoma by FT-Raman spectroscopy

“…The authors discussed, based on these observations, that the neoplastic transformation in the tissues, can start cellular changes no matter FIGURE 3 -The discriminative analysis of data was able to differ 93.1% of the variables. Through two imaginary lines, the separation of the groups by similarity can be observed the kind of tissue involved and the alterations on the amide bands in the Raman spectra are attributed to the protein conformation 10 . In the melanoma group of this study, the alterations visually examined were similar, diminishing on the Amide I band, with spectral area distortion between 1640 and 1670 cm -1 .…”

Design and characterization of a novel multimodal fiber-optic probe and spectroscopy system for skin cancer applications

“…[49][50][51][52][53][54][55] The first processing step involves background subtraction whereby a dark spectrum is subtracted from the raw spectrum. Second, the wavelength and intensity calibrations are performed in a very similar manner as described for the fluorescence spectra; the only difference is that the peak fitting algorithm is used to find Tylenol pixel locations (instead of HgAr lines as needed for the fluorescence).…”