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

来源：

COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2018, PT II | 2018年 / 11056卷

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

页码：489 / 498

页数：10

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