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

JOURNAL OF BIOPHOTONICS(2024)

引用 0|浏览4
暂无评分
摘要
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. This study unveils the potential of melanoma diagnostics through the exploration of cellular autofluorescence. Endogenous fluorophores, including NAD(P)H and flavins, are investigated using multispectral imaging. Immortalized melanoma and healthy fibroblasts cell lines are differentiated, and results are validated on patient cells. This non-invasive tool offers improved melanoma detection, shedding light on underlying biological processes.image
更多
查看译文
关键词
autofluorescence,feature analysis,fibroblasts,label-free,machine learning,melanoma,multispectral
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要