Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.
Anal Bioanal Chem
; 416(23): 5089-5096, 2024 Sep.
Article
en En
| MEDLINE
| ID: mdl-39017700
ABSTRACT
As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Espectrometría Raman
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Inteligencia Artificial
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Exosomas
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Detección Precoz del Cáncer
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Neoplasias Pulmonares
Límite:
Humans
Idioma:
En
Revista:
Anal Bioanal Chem
Año:
2024
Tipo del documento:
Article
País de afiliación:
China