Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSupervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra
Anno di pubblicazione2020
  • Elettronico
  • Cartaceo
Autore/iBaria, Enrico; Cicchi, Riccardo; Malentacchi, Francesca; Mancini, Irene; Pinzani, Pamela; Pazzagli, Marco; Pavone, Francesco S.
Affiliazioni autoriDepartment of Physics, University of Florence, Sesto Fiorentino, Italy; European Laboratory for Non-Linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy; National Institute of Optics, National Research Council, Florence, Italy; Department of biomedical, experimental, and clinical sciences "Mario Serio", University of Florence, Florence, Italy
Autori CNR e affiliazioni
  • inglese
AbstractMalignant 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.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da202000365-1
Pagine a202000365-8
Pagine totali8
RivistaJournal of biophotonics (Print)
Attiva dal 2008
Editore: Wiley-VCH-Verl. - Weinheim
Paese di pubblicazione: Germania
Lingua: inglese
ISSN: 1864-063X
Titolo chiave: Journal of biophotonics (Print)
Titolo proprio: Journal of biophotonics. (Print)
Titolo abbreviato: J. biophotonics (Print)
Numero volume della rivista14
Fascicolo della rivista3
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000599732400001)
  • PubMed (Codice:33305912)
  • Scopus (Codice:2-s2.0-85097782506)
Parole chiavecells, melanoma, neural network, Raman spectroscopy, supervised learning
Link (URL, URI)
Titolo parallelo-
Scadenza embargo-
Data di accettazione10/12/2020
Note/Altre informazioni-
Strutture CNR
  • INO — Istituto nazionale di ottica
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra (documento privato )
Descrizione: VoR
Tipo documento: application/pdf