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Novel biomarkers for liver imaging in the monitoring of cancer therapy

Project ID: 2017_XXY

Student: Emily Chan

1st supervisor: Julia Schnabel, King’s College London
2nd supervisor: Ralph Sinkus, King’s College London
Clinical supervisor: Vicky Goh, King’s College London
Industry supervisors:  Cat Kelly, Matt Kelly and Rajarshi Banerjee, Perspectum Diagnostics

The aim of this project is to develop novel imaging biomarkers to characterise precancerous liver disease (cirrhosis) for patients under surveillance, using a range of novel quantitative magnetic resonance imaging (MRI)  techniques alongside routine imaging, such as conventional ultrasound, and contrast-enhanced MRI or computed tomography. MR elastography (MRE) and recently quantitative MR imaging with T1-mapping have shown great promises in predicting  fibrosis stages in patients.  This project will utilise their complementarity in characterising cirrhosis by developing dedicated computational medical image analysis technology that allows clinicians to spatially correlate and follow-up patient- and disease-specific tissue characteristics. This project will generate  clinically relevant imaging biomarkers for early detection of hepatocellular carcinoma (HCC). The ultimate goal of this research is to detect changes in tissue characteristics from precancerous to cancerous states, which could impact on a range of other primary and secondary cancers.

Source: perspectum-diagnostics.com

As part of this project, a range of novel quantitative MRI techniques alongside clinical routine imaging will be conducted in the Department of Radiology, as part of their standard of care, with clinical support from the Gastroeneterology and Hepatology teams. The project will require the development of dedicated computational tools that can compensate for tissue motion/deformation between different types of scans, and scans that are acquired longitudinally, by taking into account imaging-derived patient- and disease-specific tissue or tissue perfusion parameters, in order to detect (and ultimately predict) changes in tissue characteristics from precancerous to cancerous states. This project contains a very strong translational component, by embedding the developed techniques into a commercial software product for rapid deployment into clinical practice.


Figure 2: MRE applied to a liver cancer patient with hepatocellular carcinoma. A: high resolution T2-weighted MRI depicting a large tumour in the liver. B: Corresponding dynamic contrast-enhanced MR image showing the relative contrast agent (CA) enhancement [%] 2mins after bolus injection. C: Corresponding MRE shear wave image. D: Image of the magnitude of the complex shear modulus G*.
This is an MRC industrial CASE project with Perspectum Diagnostics at Oxford

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