Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/71393
Título: | A MRI view of brain tumor outcome prediction |
Autor(es): | Neto, Cristiana Dias, Inês Santos, Maria Alves, Victor Ferraz, Filipa Tinoco Neves, João Vicente, Henrique Neves, José |
Palavras-chave: | 3D slicer Brain tumor Brain tumor outcome prediction Case-based reasoning Feature extraction Knowledge representation and reasoning Logic programming Magnetic resonance imaging |
Data: | 2019 |
Editora: | Springer, Cham |
Revista: | Advances in Science, Technology and Innovation |
Citação: | Neto C. et al. (2019) A MRI View of Brain Tumor Outcome Prediction. In: Mateev M., Poutziouris P. (eds) Creative Business and Social Innovations for a Sustainable Future. Advances in Science, Technology & Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. https://doi.org/10.1007/978-3-030-01662-3_1 |
Resumo(s): | On the one hand, cancer and tumor are one of the most feared terms in today’s society. It refers to an unstable growth of cells that potentially invade the surrounding tissues and may eventually lead to edema or even death. On the other hand, the term tumor is often misleading since people assume that it is the same as cancer, but this is not necessarily true. A cancer is a particularly threatening type of tumor. The word tumor simply refers to a mass, and in particular a brain tumor is a mass located in the patient’s brain that may seriously threaten his/her life. Thus, it is crucial to study which factors may influence the outcome of a brain tumor to improve the given treatment or even make the patient more contented. Therefore, this study presents a decision support system based on Magnetic Resonance Imaging (MRI) data or knowledge (if the data is presented in context) that allows for brain tumor outcome prediction. It describes an innovative approach to cater for brain illness where Logic Programming comes in support of a computational approach based on Case Based Reasoning. An attempt is made to predict whether a patient will die or survive with or without a tumor, where the data or knowledge may be of type unknown, incomplete or even self-contradictory. |
Tipo: | Capítulo de livro |
URI: | https://hdl.handle.net/1822/71393 |
ISBN: | 978-3-030-01661-6 |
e-ISBN: | 978-3-030-01662-3 |
DOI: | 10.1007/978-3-030-01662-3_1 |
ISSN: | 2522-8714 |
Versão da editora: | https://link.springer.com/chapter/10.1007%2F978-3-030-01662-3_1 |
Acesso: | Acesso restrito UMinho |
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Ficheiro | Descrição | Tamanho | Formato | |
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8.pdf Acesso restrito! | 1,4 MB | Adobe PDF | Ver/Abrir |