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https://hdl.handle.net/10316/108105
Título: | An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images | Autor: | Nogueira, Mariana A. Abreu, Pedro H. Martins, Pedro Machado, Penousal Duarte, Hugo Santos, João |
Palavras-chave: | Artificial neural networks; Images descriptors; PET/CT images; Treatment response assessment | Data: | 13-Fev-2017 | Editora: | Springer Nature | Projeto: | project NORTE-01-0145-FEDER-000027, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). | Título da revista, periódico, livro ou evento: | BMC Medical Imaging | Volume: | 17 | Número: | 1 | Resumo: | 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. | URI: | https://hdl.handle.net/10316/108105 | ISSN: | 1471-2342 | DOI: | 10.1186/s12880-017-0181-0 | Direitos: | openAccess |
Aparece nas coleções: | FCTUC Eng.Informática - Artigos em Revistas Internacionais I&D CISUC - Artigos em Revistas Internacionais |
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