Ensemble Strategies for EGFR Mutation Status Prediction in Lung Cancer

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Malafaia, M (Autor) Outra; Pereira, T (Autor) Outra; Silva, F (Autor) Outra; Morgado, J (Autor) Outra; Cunha, A (Autor) Outra; Oliveira, HP (Autor) FCUP

Ata de Conferência Internacional

Título: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021

Páginas: 3285-3288

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00V-V0X

DOI: 10.1109/embc46164.2021.9629755

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4