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.
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.
People |
ORCID iD |
| Julia Schnabel (Primary Supervisor) |
Publications
De Luca V
(2018)
Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins.
in Medical physics
Kallehauge JF
(2017)
Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI.
in Magnetic resonance in medicine
Kannan P
(2018)
Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases.
in Clinical cancer research : an official journal of the American Association for Cancer Research
Papiez BW
(2018)
GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.
in Journal of medical imaging (Bellingham, Wash.)
Roque T
(2018)
A DCE-MRI Driven 3-D Reaction-Diffusion Model of Solid Tumor Growth.
in IEEE transactions on medical imaging
Rueckert D
(2020)
Model-Based and Data-Driven Strategies in Medical Image Computing
in Proceedings of the IEEE
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/ |