A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma.
PLoS One
; 7(3): e34457, 2012.
Article
em En
| MEDLINE
| ID: mdl-22461913
ABSTRACT
PURPOSE:
Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.RESULTS:
The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis. CONCLUSIONS AND CLINICAL RELEVANCE The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Adenocarcinoma
/
Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz
/
Proteômica
/
Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limite:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
China