Scientific Results

Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra

Year: 2020

Authors: Baria E., Cicchi R., Malentacchi F., Mancini I., Pinzani P., Pazzagli M., Pavone FS.

Autors Affiliation: Univ Florence, Dept Phys, I-50019 Sesto Fiorentino, Italy; Univ Florence, European Lab Nonlinear Spect, Sesto Fiorentino, Italy; CNR, Natl Inst Opt, Florence, Italy; Univ Florence, Dept Biomed Expt & Clin Sci Mario Serio, Florence, Italy

Abstract: Malignant melanoma is an aggressive form of skin cancer, which develops from the genetic mutations of melanocytes – the most frequent involving BRAF and NRAS genes. The choice and the effectiveness of the therapeutic approach depend on tumour mutation; therefore, its assessment is of paramount importance. Current methods for mutation analysis are destructive and take a long time; instead, Raman spectroscopy could provide a fast, label-free and non-destructive alternative. In this study, confocal Raman microscopy has been used for examining three in vitro melanoma cell lines, harbouring different molecular profiles and, in particular, specific BRAF and NRAS driver mutations. The molecular information obtained from Raman spectra has served for developing two alternative classification algorithms based on linear discriminant analysis and artificial neural network. Both methods provide high accuracy (>= 90%) in discriminating all cell types, suggesting that Raman spectroscopy may be an effective tool for detecting molecular differences between melanoma mutations.


Volume: DEC 2020      Pages from: 202000365-1  to: 202000365-8

KeyWords: cells; melanoma; neural network; Raman spectroscopy; supervised learning
DOI: 10.1002/jbio.202000365

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