An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images

• Autor(a) principal: Nogueira, Mariana A.
• Data de Publicação: 2017
• Outros Autores: Abreu, Pedro H., Martins, Pedro, Machado, Penousal, Duarte, Hugo, Santos, João
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/108105; https://doi.org/10.1186/s12880-017-0181-0
• topic: Artificial neural networks; Images descriptors; PET/CT images; Treatment response assessment; Algorithms; Female; Hodgkin Disease; Humans; Male; Neural Networks, Computer; Neuroendocrine Tumors; Pattern Recognition, Automated; Positron Emission Tomography Computed Tomography; Reproducibility of Results; Treatment Outcome; Whole Body Imaging

Background: Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods: In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results: The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions: After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.