The University of Southampton
University of Southampton Institutional Repository

Agent-Based Distributed Decision Support System for Brain Tumour Diagnosis and Prognosis

Gonzalez-Velz, H, Mier, M, Arus, C, Celda, B, van Huffel, S, Lewis, Paul, Peet, A and Robles, M (2006) Agent-Based Distributed Decision Support System for Brain Tumour Diagnosis and Prognosis At International Conference on Multidisciplinary Information Sciences and Technologies, Spain. , pp. 288-292.

Record type: Conference or Workshop Item (Paper)


Brain tumours remain an important cause of morbidity and mortality in Europe. Diagnosis using Magnetic Resonance Imaging (MRI) is non-invasive, but only achieves 60-90% accuracy depending on the tumour type and grade. The current gold standard classification of brain tumours by biopsy and histopathological analysis involves invasive surgical procedure and incurs a risk. Nowadays the diagnosis and treatment of brain tumours is typically based on clinical symptoms, radiological appearance and often a histopathological diagnosis of a biopsy. However, treatment response of histologically or radiologically-similar tumours can vary widely, particularly in children. Magnetic Resonance Spectroscopy (MRS) is a non-invasive technique for determining the tissue biochemical composition (metabolomic profile) of a tumour. Additionally, the genomic profile, determined using DNA microarrays, facilitates the classification of tumour grades and types not trivially distinguished by morphologic appearance. Thus, we propose the definition of a decision support system (DSS) which employs MRS and genomic profiles. This DSS will deploy an ad hoc agent-based architecture in order to negotiate a distributed diagnostic tool for brain tumours, implement data mining techniques, transfer clinical data and extract information. The distributed nature of our approach will help the users to observe local centre policies for sharing information whilst allowing them to benefit from the use of distributed data warehouse (d-DWH). Moreover, it will permit the design of local classifiers targeting a specific patient population. We argue that this new information for classifying tumours along with clinical data, should be securely and easy accessible in order to improve the diagnosis and prognosis of tumours. All data will be stored anonymously, and securely through a network of data marts based on all this information acquired and stored at centres throughout Europe. This network will grant bona-fide access to an organisation in return for its contribution of clinical data to a d-DWH/Decision Support System (d-DSS). This rest of this paper is structured as follows. First, we provide some background on the underlying technologies for this project: brain tumour detection and agent technology. Then we provide the architectural specification. Finally, we conclude with our future work.

PDF Agent_based_distributed_decision_support_system_for_brain_tumour_diagnosis_and_prognosis.pdf - Other
Download (175kB)

More information

Published date: October 2006
Additional Information: Event Dates: October 2006
Venue - Dates: International Conference on Multidisciplinary Information Sciences and Technologies, Spain, 2006-10-01
Organisations: Web & Internet Science


Local EPrints ID: 267044
ISBN: 84-611-3104-5
PURE UUID: 8e4040d8-7856-40dc-9c98-f2d3d474637c

Catalogue record

Date deposited: 20 Jan 2009 11:50
Last modified: 18 Jul 2017 07:09

Export record


Author: H Gonzalez-Velz
Author: M Mier
Author: C Arus
Author: B Celda
Author: S van Huffel
Author: Paul Lewis
Author: A Peet
Author: M Robles

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.