Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis.
Aline KnabAyad G AnwerBernadette PedersenShannon HandleyAbhilash Goud MarupallyAbbas HabibalahiEwa M GoldysPublished in: Journal of biophotonics (2024)
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.
Keyphrases
- induced apoptosis
- cell cycle arrest
- photodynamic therapy
- high resolution
- fluorescence imaging
- label free
- skin cancer
- machine learning
- deep learning
- signaling pathway
- stem cells
- oxidative stress
- air pollution
- gene expression
- genome wide
- dna methylation
- risk assessment
- magnetic resonance imaging
- pi k akt
- computed tomography
- artificial intelligence
- convolutional neural network
- climate change
- mesenchymal stem cells
- data analysis