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Content archived on 2024-06-20

Agent-based Distributed Decision Support System for brain tumour diagnosis and prognosis


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 micro-arrays, facilitates the classification of tumour grades and types not trivially distinguished by morphologic appearance. 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 distributed Data Warehouse (d-DWH)/Decision Support System (d-DSS). An ad hoc agent-based architecture will 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 the d-DWH.

Moreover, it will permit the design of local centres targeting a specific patient population. We will form a consortium encompassing a wide range of talent from Universities, SME and Hospitals. The d-DWH/d-DSS, the MRS installed base and the expertise of the consortium will provide the foundations for the first EU grid for brain tumour diagnosis and prognosis. We will profit from a world-class clinical data exchange network to address one of the most pernicious diseases of our time: cancer

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Participants (8)