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 optimised treatments.
Magnetic Resonance Spectroscopy (MRS) is a non-invasive technique for determining tissue biochemicals (the metabolomic profile). MRS can be performed along with clinical MR Imaging but widespread use is hampered by specialised analysis requirements and poor dissemination of the skills needed to interpret the data. 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.
Fields of science
- natural sciencescomputer and information sciencessoftware
- natural sciencesbiological sciencesgeneticsDNA
- natural sciencescomputer and information sciencesartificial intelligencepattern recognition
- engineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging
- natural sciencesphysical sciencesopticsspectroscopy
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