Ensemble Strategies for EGFR Mutation Status Prediction in Lung Cancer.
Annu Int Conf IEEE Eng Med Biol Soc
; 2021: 3285-3288, 2021 11.
Article
em En
| MEDLINE
| ID: mdl-34891942
Lung cancer treatments that are accurate and effective are urgently needed. The diagnosis of advanced-stage patients accounts for the majority of the cases, being essential to provide a specialized course of treatment. One emerging course of treatment relies on target therapy through the testing of biomarkers, such as the Epidermal Growth Factor Receptor (EGFR) gene. Such testing can be obtained from invasive methods, namely through biopsy, which may be avoided by applying machine learning techniques to the imaging phenotypes extracted from Computerized Tomography (CT). This study aims to explore the contribution of ensemble methods when applied to the prediction of EGFR mutation status. The obtained results translate in a direct correlation between the semantic predictive model and the outcome of the combined ensemble methods, showing that the utilized features do not have a positive contribution to the predictive developed models.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Receptores ErbB
/
Neoplasias Pulmonares
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article