Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis.
Photodiagnosis Photodyn Ther
; 42: 103544, 2023 Jun.
Article
en En
| MEDLINE
| ID: mdl-37004836
ABSTRACT
Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically discovered when it is already advanced. In this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Fotoquimioterapia
/
Neoplasias de la Vesícula Biliar
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Photodiagnosis Photodyn Ther
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
TERAPEUTICA
Año:
2023
Tipo del documento:
Article