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Book ChapterDOI

A Case-Based Reasoning Approach to GBM Evolution

05 Sep 2018-pp 489-498

TL;DR: 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.

AbstractGlioBastoma 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.

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Summary

  • 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.

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A Case-Based Reasoning Approach to GBM
Evolution
Ana Mendonça
1
, Joana Pereira
1
, Rita Reis
1
, Victor Alves
2
,
António Abelha
2
, Filipa Ferraz
1,2
,João Neves
3
,
Jorge Ribeiro
4
, Henrique Vicente
2,5
, and José Neves
2(&)
1
Departamento de Informática, Escola de Engenharia, Universidade do Minho,
Braga, Portugal
{a70606,a73302,a71983}@alunos.uminho.pt,
filipatferraz@gmail.com
2
Centro Algoritmi, Universidade do Minho, Braga, Portugal
{valves,jneves}@di.uminho.pt
3
Mediclinic Arabian Ranches, PO Box 282602, Dubai, United Arab Emirates
joaocpneves@gmail.com
4
Escola Superior de Tecnologia e Gestão,
Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
jribeiro@estg.ipvc.pt
5
Departamento de Química, Escola de Ciências e Tecnologia,
Centro de Química de Évora, Universidade de Évora, Évora, Portugal
hvicente@uevora.pt
Abstract. 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 patients GBM volume after ve
months of treatment based on features extracted from MR images and patient
attributes such as age, gender, and type of treatment.
Keywords: Articial Intelligence
GlioBlastoma Multiforme
Logic Programming
Knowledge Representation and Reasoning
Case Based Reasoning
1 Introduction
Brain tumors are dened as abnormal cells that tend to proliferate without control,
being possible to group the different types of brain tumors into two different classes,
i.e., primary and secondary tumors. The former one develops in the brain itself, while
secondary brain tumors spread through metastasis from other locations to the brain [1].
GlioBlastoma Multiforme (GBM) is most common about 30% of primary brain
© Springer Nature Switzerland AG 2018
N. T. Nguyen et al. (Eds.): ICCCI 2018, LNAI 11056, pp. 489498, 2018.
https://doi.org/10.1007/978-3-319-98446-9_46
Citations
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Dissertation
12 Feb 2016
Abstract: Dans cette these nous analysons dans quelle mesure le concept de mobilite peut etre pris en compte dans la demarche d'analyse et de conception de systemes de production. Notre apport vise a formaliser la demarche d'analyse et de conception de ce dernier, explicitant les decisions a prendre, les informations necessaires et les criteres de decision a mettre en place. Dans cet objectif, deux niveaux d'analyse ont ete distingues : un niveau local concernant un site de production et un niveau global comprenant un ensemble de sites.Le premier niveau local considere un seul site de production. A ce niveau nous avons propose une approche prenant en compte les caracteristiques du site de production. Dans notre contexte, le choix de la localisation geographique de production est impose par le client. De ce fait, la conception du systeme de production doit s'adapter a cette contrainte. D'un point de vue conception, quatre questions sont abordees : (1) dans quelle mesure le concept de mobilite peut etre integre dans une demarche de conception de systeme de production mobile ? (2) quelles caracteristiques de l'environnement de production doivent etre prises en compte ? (3) comment determiner ce qu'il faut produire sur site ou ce qu'il serait opportun d'externaliser ? et (4) compte tenu des informations obtenues quelle est la meilleure configuration du SPM a envisager et selon quels criteres de choix ? La reponse a ces questions conduit a la proposition d'une configuration du SPM adaptee pour un seul site de production.Le deuxieme niveau global traite la problematique de mobilite successive multi sites. En effet, pour etre rentabilise le systeme de production doit etre mobilise sur plusieurs sites de production. A chaque changement de site de production, une reconfiguration du systeme de production s'impose en se basant sur la configuration existante (version i-1). LaThese de Youssef BENAMAreconfigurabilite concerne d'une part l'architecture interne du systeme (choix des machines, recrutement de nouvelles equipes locales, etc) et d'autre part l'organisation de la chaine d'approvisionnement du SPM (faire en interne ou externaliser, fournisseur local, etc.). A ce niveau global d'analyse, nous proposons deux modeles d'analyse : (1) un premier modele pour l'analyse de la reconfigurabilite interne. Ce modele d'analyse permet d'adapter le nombre de lignes de production et le nombre d'equipes en fonction d'un scenario de demande (localisations geographiques, capacite necessaire par site). L'originalite de notre proposition consiste d'une part en l'evaluation des couts de reconfiguration necessaires et d'autre part l'appreciation du niveau d'adequation de la configuration proposee avec le contexte du site de production via l'utilisation de l'indicateur de mobilite. (2) Le deuxieme modele d'analyse concerne la reconfigurabilite de la chaine d'approvisionnement amont du SPM. Il consiste en une adaptation du modele d'aide a la decision "faire ou faire faire" par l'integration d'un cote de l'importance du site de production et d'un autre cote des specificites de chaque site de production.La demarche d'analyse proposee est illustree sur le cas industriel concernant la conception d'une usine mobile pour la fabrication et l'installation sur site de composants de centrales solaires thermodynamiques.

8 citations


References
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Journal ArticleDOI
TL;DR: An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.
Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

5,554 citations


01 Jan 2012
TL;DR: In this research work, four different classes of brain tumors are used and the GLCM based textural features of each class are extracted and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.
Abstract: Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal) may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.

147 citations


Journal ArticleDOI
TL;DR: This paper proposes a fuzzy ontology-based CBR framework that combines a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types and achieves an accuracy of 97.67%.
Abstract: Propose a fuzzy ontology based semantic-CBR framework.Propose a novel OWL2 fuzzy case-base ontology.Propose a fuzzy semantic case retrieval algorithm using an SNOMED CT fragment.Implement the fuzzy KI-CBR system using diabetes diagnosis as a case study.Combine fuzzy logic and ontology semantics in CBR enhances the CBR accuracy. ObjectiveCase-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. MethodsKnowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. MaterialThis framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. ResultsThe resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. ConclusionBuilding an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems.

79 citations


Proceedings ArticleDOI
José Neves1, José Machado1, Cesar Analide1, António Abelha1, Luís Brito1 
03 Dec 2007
Abstract: In this paper we address the role of divergence and convergence in creative processes, and argue about the need to consider them in Computational Creativity research in the Genetic or Evolutionary Programming paradigm, being one's goal the problem of the Halt Condition in Genetic Programming. Here the candidate solutions are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried out by those logical theories or programs. Furthermore, we present Conceptual Blending Theory as being a promising framework for implementing convergence methods within creativity programs, in terms of the logic programming framework.

75 citations


Journal ArticleDOI
TL;DR: UMCourt is described, a project built around two sub-fields of AI research: Multi-agent Systems and Case-Based Reasoning, aimed at fostering the development of tools for ODR, to develop autonomous tools that can increase the effectiveness of the dispute resolution processes.
Abstract: The growing use of Information Technology in the commercial arena leads to an urgent need to find alternatives to traditional dispute resolution. New tools from fields such as artificial intelligence (AI) should be considered in the process of developing novel online dispute resolution (ODR) platforms, in order to make the ligation process simpler, faster and conform with the new virtual environments. In this work, we describe UMCourt, a project built around two sub-fields of AI research: Multi-agent Systems and Case-Based Reasoning, aimed at fostering the development of tools for ODR. This is then used to accomplish several objectives, from suggesting solutions to new disputes based on the observation of past similar disputes, to the improvement of the negotiation and mediation processes that may follow. The main objective of this work is to develop autonomous tools that can increase the effectiveness of the dispute resolution processes, namely by increasing the amount of meaningful information that is available for the parties.

70 citations