Novel Biomarkers for Liver Imaging in the Monitoring of Cancer Therapy

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering

Abstract

Hepatocellular carcinoma (HCC) causes approximately 1500 UK deaths per year and the incidence is rising, predominantly due to lifestyle factors. Recently developed non-invasive quantitative magnetic resonance imaging (MRI) techniques, such as corrected T1 mapping and MR elastography (commercialised as LiverMultiScan and Resoundant respectively) have shown promise in characterising liver fibrosis (Banerjee et al. J Hep (2014), Huwart et al. (2008)). However, their potential in characterising liver cirrhosis under surveillance, as a means to predict onset of HCC, has not yet been explored, and could complement and enhance routine imaging, such as ultrasound (US), single-phase contrast-enhanced
computed tomography (CE-CT), diffusion MRI or multi-phasic CE-MRI commonly used for such patients.
This studentship proposal aims at developing novel imaging biomarkers to characterise precancerous liver disease (cirrhosis) under surveillance, using a range of novel quantitative MRI techniques alongside routine imaging. Imaging will be conducted in the Department of Radiology, as part of their standard of care, with clinical support from the Gastroeneterology and Hepatology team. 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 software product for rapid deployment into clinical practice.
PhD Roadmap (it is proposed to embed / strongly align this studentship with the EPSRC Centre for Doctoral Training (CDT) in Medical Imaging, which is run jointly between King's College London and Imperial College London): 1st year delivery: MRes in Medical Imaging Sciences, including course options on image acquisition & reconstruction, imaging chemistry & biology, and medical image computing & computational modelling; MRes project on "Tools for HCC detection in T1 mapping" at Perspectum Diagnostics.
2nd year delivery: Development of deformable registration of the liver for routine imaging of patients with cirrhosis. Prototype for co-registration of routine imaging (US or CE-CT/MR) to T1 and MR elastography imaging. Conference/workshop publication on methodology.
3rd year delivery: Collation of clinical data. Full comparison and performance evaluation of different T1 mapping techniques for patients with cirrhosis under surveillance, and correlation against MR elastography findings. Technical/clinical conference/journal publication(s).
4th year delivery: Complete software integration, write up Thesis and additional publications with clinical evaluation.

Studentship Projects

Project Reference Relationship Related To Start End Student Name
MR/N018028/1 31/03/2017 29/09/2021
1870061 Studentship MR/N018028/1 31/03/2017 29/09/2021
 
Description EPSRC Centre for Doctoral Training in Smart Medical Imaging at King's College London and Imperial College London
Amount £6,022,394 (GBP)
Funding ID EP/S022104/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 08/2019 
End 03/2028
 
Description London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare
Amount £9,985,272 (GBP)
Funding ID 104691 
Organisation Innovate UK 
Sector Public
Country United Kingdom
Start 02/2019 
End 01/2022
 
Description Perspectum Ltd 
Organisation Perspectum
Country United Kingdom 
Sector Private 
PI Contribution Joint PhD project between King's College London and Perspectum Diagnostics. We have installed PD's LiverMultiscan product on our clinical research scanners, to help evaluate their product and test against our own quantitative MR image sequences.
Collaborator Contribution Joint PhD project between King's College London and Perspectum Diagnostics. PD has installed LiverMultiscan product on our clinical research scanners, contributes own data and co-supervision of the student, in addition to financial support of this MRC CASE studentship.
Impact See publications attributed to this award
Start Year 2017
 
Description CRUK/EPSRC Early Detection Sandpit - 7 January 2019, Oxford 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Invited panel talk & discussion on "AI for Early Detection" - Prof Julia Schnabel
Year(s) Of Engagement Activity 2019
 
Description Case CCC Artificial Intelligence in Oncology Symposium 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact Invited keynote on "AI for Cancer Imaging"
Year(s) Of Engagement Activity 2021
URL https://case.edu/cancer/events/artificial-intelligence-oncology/aio-agenda
 
Description Medical Imaging Summer School (MISS 2018): Medical Imaging meets Deep Learning - Director Prof Julia Schnabel 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact The focus of this Medical Imaging Summer School (MISS) is to train a new generation of young scientists to bridge this gap, by providing insights into the various interfaces between medical imaging and deep learning, based on the shared broad categories of medical image computing, computer-aided image interpretation and disease classification. The course will contain a combination of in-depth tutorial-style lectures on fundamental state-of-the-art concepts, followed by accessible yet advanced research lectures using examples and applications. A broad overview of the field will be given, and guided reading groups will complement lectures. The course will be delivered by world renowned experts from both academia and industry, who are working closely at the interface of medical imaging/deep learning.

The school aims to provide a stimulating graduate training opportunity for young researchers and Ph.D. students. The participants will benefit from direct interaction with world leaders in medical image computing and deep learning(often working in both fields). Participants will also have the opportunity to present their own research, and to interact with their scientific peers, in a friendly and constructive setting.
Year(s) Of Engagement Activity 2018
URL http://iplab.dmi.unict.it/miss18/