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Contributed by Coordinator
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Monday, 08 November 2004 |
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Actually, diagnosis and treatment of brain tumours is based on clinical symptoms, radiological appearance, and often a histopathological diagnosis of a biopsy. However, treatment response of histological or radiologically similar tumours can vary widely, particularly for childhood tumours. New technologies are available that may improve tumour classification in terms of diagnosis and prognosis, and may allow individually optimized treatments.
The genomic profile of tumours can be determined with DNA microarrays. Early studies have demonstrated differences in gene expression between tumour grades and between tumour types not easily distinguished by morphologic appearance.
We will bring together the expertise required to study the genomic and metabolomic characteristics of brain tumours, with a multi-centre collaboration to acquire statistically significant data, particularly for rare tumour types. Clinical MRS, high-resolution 1H MRS and gene array analysis of biopsies, will be used to investigate how metabolomic and genomic profiles relate to clinically relevant factors such as survival time and treatment response. As well as providing new scientific data on tumour biology, we will develop the technology for this information to be readily and easily used to help radiologists and neurosurgeons in the management and treatment of brain tumour patients. We will build upon expertise obtained with INTERPRET EU project IST-1999-10310, which created a MRS decision support tool (DSS) for tumour diagnosis. A new web-accessible DSS will be developed, incorporating genomic and metabolomic data, and its diagnostic performance validated in a clinical demonstration of added value.
The eTUMOUR project has two main goals:
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Development of a web-accessible Decision Support System (DSS) that has a Graphical User Interface (GUI) to display clinical, metabolomic and genetic brain tumor data, combined with a database of rigorously validated and anonymised MRI/MRS, clinical, histological and molecular phenotype data from patients.
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Facilitation of evidence-based clinical decision-making (e.g. diagnosis, prognosis, optimal treatment strategies etc.) by using statistical pattern recognition analysis of molecular images of brain tumours (using MRS) and incorporating new criteria such as genetic based tumour classifications and related clinical information such as patient outcome. |
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Last Updated ( Monday, 20 December 2004 )
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