Details
Name
Francisco Carvalho SilvaCluster
Networked Intelligent SystemsRole
Research AssistantSince
02nd December 2019
Nationality
PortugalCentre
Telecommunications and MultimediaContacts
+351222094000
francisco.c.silva@inesctec.pt
2022
Authors
Sousa, J; Pereira, T; Silva, F; Silva, MC; Vilares, AT; Cunha, A; Oliveira, HP;
Publication
Applied Sciences
Abstract
2022
Authors
Silva, F; Pereira, T; Neves, I; Morgado, J; Freitas, C; Malafaia, M; Sousa, J; Fonseca, J; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Costa, JL; Hespanhol, V; Cunha, A; Oliveira, HP;
Publication
Journal of Personalized Medicine
Abstract
2021
Authors
Pereira, T; Freitas, C; Costa, JL; Morgado, J; Silva, F; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Cunha, A; Oliveira, HP;
Publication
Journal of Clinical Medicine
Abstract
2021
Authors
Morgado, J; Pereira, T; Silva, F; Freitas, C; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Costa, JL; Cunha, A; Oliveira, HP;
Publication
Applied Sciences
Abstract
2021
Authors
Silva, F; Pereira, T; Morgado, J; Frade, J; Mendes, J; Freitas, C; Negrao, E; De Lima, BF; Da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Costa, JL; Cunha, A; Oliveira, HP;
Publication
IEEE Access
Abstract
Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification. CCBYNCND
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