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.

Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra / Baria, Enrico; Cicchi, Riccardo; Malentacchi, Francesca; Mancini, Irene; Pinzani, Pamela; Pazzagli, Marco; Pavone, Francesco S. - In: JOURNAL OF BIOPHOTONICS. - ISSN 1864-063X. - ELETTRONICO. - 14:(2021), pp. 202000365.0-202000365.0. [10.1002/jbio.202000365]

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

Baria, Enrico
;
Cicchi, Riccardo
Conceptualization
;
Malentacchi, Francesca
Methodology
;
Mancini, Irene
Methodology
;
Pinzani, Pamela
Conceptualization
;
Pavone, Francesco S
Writing – Original Draft Preparation
2021

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.
14
0
0
Goal 3: Good health and well-being
Baria, Enrico; Cicchi, Riccardo; Malentacchi, Francesca; Mancini, Irene; Pinzani, Pamela; Pazzagli, Marco; Pavone, Francesco S
File in questo prodotto:
File Dimensione Formato  
Journal of Biophotonics - 2020 - Baria - Supervised learning methods for the recognition of melanoma cell lines 2020.pdf

accesso aperto

Tipologia: Pdf editoriale (Version of record)
Licenza: Open Access
Dimensione 2.48 MB
Formato Adobe PDF
2.48 MB Adobe PDF Visualizza/Apri

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1288224
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 3
social impact