On the way for the best imaging features from CT images to predict <i>EGFR</i> Mutation Status in Lung Cancer

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Silva, P (Autor) Outra; Pereira, T (Autor) Outra; Teixeira, M (Autor) Outra; Silva, F (Autor) Outra; Oliveira, HP (Autor) FCUP

Ata de Conferência Internacional

Título: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022

Páginas: 2659-2662

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00X-BGQ

DOI: 10.1109/embc48229.2022.9871911

Abstract (EN): Artificial Intelligence-based tools have shown promising results to help clinicians in diagnosis tasks. Radio-genomics would aid in the genotype characterization using information from radiologic images. The prediction of the mutations status of main oncogenes associated with lung cancer will help the clinicians to have a more accurate diagnosis and a personalized treatment plan, decreasing the need to use the biopsy. In this work, novel and objective features were extracted from the lung that contained the nodule, and several machine learning methods were combined with feature selection techniques to select the best approach to predict the EGFR mutation status in lung cancer CT images. An AUC of 0.756 ± 0.055 was obtained using a logistic regression and independent component analysis as feature selector, supporting the hypothesis that CT images can capture pathophysiological information with great value for clinical assessment and personalized medicine of lung cancer. Clinical Relevance-Radiogenomic approaches could be an interesting help for lung cancer characterization. This work represents a preliminary study for the development of computer-aided decision systems to provide a more accurate and fast characterization of lung cancer which is fundamental for an adequate treatment plan for lung cancer patients.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4