A Case-Based Reasoning Approach to GBM Evolution

• Autor(a) principal: Mendonça, Ana
• Data de Publicação: 2018
• Outros Autores: Pereira, Joana, Reis, Rita, Alves, Victor, Abelha, António, Ferraz, Filipa, Neves, João, Ribeiro, Jorge, Vicente, Henrique, Neves, José
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/23395; https://doi.org/10.1007/978-3-319-98446-9_46
• topic: Artificial Intelligence; GlioBlastoma Multiforme; Logic Programming; Knowledge Representation and Reasoning; Case Based Reasoning

GlioBastoma Multiforme (GBM) is an aggressive primary brain tumor characterized by a heterogeneous cell population that is genetically unstable and resistant to chemotherapy. Indeed, despite advances in medicine, patients diagnosed with GBM have a median survival of just one year. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for determining the location and size of brain tumors. Indisputably, this technique plays a major role in the diagnosis, treatment planning, and prognosis of GBM. Therefore, this study proposes a new Case Based Reasoning approach to problem solving that attempts to predict a patient’s GBM volume after five months of treatment based on features extracted from MR images and patient attributes such as age, gender, and type of treatment.