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This study aims to investigate if lipid composition mapping using magnetic resonance imaging could improve early and accurate cancer detection in genetic mutation carriers at high risk of breast cancer. It is hypothesised that there is a significant difference in the extent of spatial variation in lipid composition in breast from MRI between genetic mutation carriers and patients with breast cancer.
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Breast cancer is a major and expanding societal challenge despite the significant improvement in survival rate. The current screening method has been shown to lead to overtreatment, prompting the development of accurate early detection approaches targeting underlying clinical features. Patients with BRCA1/2 and TP53 genes are at risk of breast cancer and receive annual surveillance. However, the current diagnostic approach relies on detecting the changes to the growth of tumour only once cancer is well under development. Detecting earlier changes to breast fatty tissue may detect patients earlier and more accurately.
Breast fatty tissue is composed of different fatty acids and a difference in the fatty acid composition has been shown in BRCA1/2 gene carriers. Measurement of lipid composition can be achieved using a specialist magnetic resonance spectroscopy method, but is limited to sampling a small area and is unable to provide the full picture of lipid composition distribution during early cancer growth. Recently, magnetic resonance chemical shift imaging, through combining magnetic resonance spectroscopy and imaging approaches, has been developed to provide lipid composition maps of the entire breast.
The investigators propose to perform lipid mapping in the breasts of 20 genetic mutation carriers at high risk of breast cancer and 20 patients with newly diagnosed breast cancer to examine the sensitivity of the method. The overarching aim is to improve early and accurate cancer detection in genetic mutation carriers at high risk of breast cancer through the assessment of whole breast lipid composition.
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40 participants in 2 patient groups
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Gabriel Cheung, PhD
Data sourced from clinicaltrials.gov
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