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Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis

Aline Knab*, Ayad G. Anwer, Bernadette Pedersen, Shannon Handley, Abhilash Goud Marupally, Abbas Habibalahi, Ewa M. Goldys*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

This study focuses on the use of cellular autofluorescence which visualizes the cell metabolism by monitoring endogenous fluorophores including NAD(P)H and flavins. It explores the potential of multispectral imaging of native fluorophores in melanoma diagnostics using excitation wavelengths ranging from 340 nm to 510 nm and emission wavelengths above 391 nm. Cultured immortalized cells are utilized to compare the autofluorescent signatures of two melanoma cell lines to one fibroblast cell line. Feature analysis identifies the most significant and least correlated features for differentiating the cells. The investigation successfully applies this analysis to pre-processed, noise-removed images and original background-corrupted data. Furthermore, the applicability of distinguishing melanomas and healthy fibroblasts based on their autofluorescent characteristics is validated using the same evaluation technique on patient cells. Additionally, the study tentatively maps the detected features to underlying biological processes. This research demonstrates the potential of cellular autofluorescence as a promising tool for melanoma diagnostics.

Original languageEnglish
Article numbere202300402
Pages (from-to)1-9
Number of pages9
JournalJournal of Biophotonics
Volume17
Issue number4
DOIs
Publication statusPublished - Apr 2024

Bibliographical note

Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • autofluorescence
  • feature analysis
  • fibroblasts
  • label-free
  • machine learning
  • melanoma
  • multispectral

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