Gonzalez-Velz, H, Mier, M, Arus, C, Celda, B, van Huffel, S, Lewis, Paul, Peet, A and Robles, M
Agent-Based Distributed Decision Support System for Brain Tumour Diagnosis and Prognosis.
In, International Conference on Multidisciplinary Information Sciences and Technologies, Merida, Spain,
Open Institute of Knowledge, .
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.
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