Lung cancer diagnosis through extracellular vesicle analysis using label-free surface-enhanced Raman spectroscopy coupled with machine learning.

ABSTRACT: Rationale: Label-free surface-enhanced Raman spectroscopy (SERS) based on extracellular vesicles (EVs) has great potential in cancer diagnosis. However, the repeatability and stability of the SERS signals and the accurate early prediction of multiple cell types based on a small number of samples still require further research. Methods: We developed a highly accurate classification approach to distinguish EVs derived from lung cancer and normal cells. This method was further validated using mixed samples of cell-derived EVs and plasma-derived EVs from both healthy and lung cancer mouse models and patients. The approach integrates label-free SERS analysis of EVs with machine learning techniques, including support vector machines (SVM) and convolutional neural networks (CNN), fo

SUBMITTER: Liu HS 

PROVIDER: S-EPMC12315814 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature