A Case-Based Reasoning Approach to GBM Evolution

被引:0
|
作者
Mendonca, Ana [1 ]
Pereira, Joana [1 ]
Reis, Rita [1 ]
Alves, Victor [2 ]
Abelha, Antonio [2 ]
Ferraz, Filipa [1 ,2 ]
Neves, Joao [3 ]
Ribeiro, Jorge [4 ]
Vicente, Henrique [2 ,5 ]
Neves, Jose [2 ]
机构
[1] Univ Minho, Escola Engn, Dept Informat, Braga, Portugal
[2] Univ Minho, Ctr Algoritmi, Braga, Portugal
[3] Mediclin Arabian Ranches, POB 282602, Dubai, U Arab Emirates
[4] Inst Politecn Viana do Castelo, Escola Super Tecnol & Gestao, Viana Do Castelo, Portugal
[5] Univ Evora, Dept Quim, Escola Ciencias & Tecnol, Ctr Quim Evora, Evora, Portugal
关键词
Artificial Intelligence; GlioBlastoma Multiforme; Logic Programming; Knowledge Representation and Reasoning; Case Based Reasoning; FRAMEWORK;
D O I
10.1007/978-3-319-98446-9_46
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:489 / 498
页数:10
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