MRC DiMeN Doctoral Training Partnership: Improving the outcome of surgical treatment for liver cancer: Combining advanced medical imaging and computational fluid dynamics for personalised treatment planning

Background: Liver surgery is the primary treatment for many patients with liver cancer, but surgical planning is often a difficult balancing act between removing enough liver tissue to completely excise the tumour, and leaving sufficient liver and blood circulation to sustain post-surgery recovery. We have recently developed a functional MRI method that can estimate function of the post-operative liver. This method is currently being assessed in a clinical trial (HEPARIM). Unfortunately, current surgery simulations do not account for patient-specific, compensatory effects in the liver and the rest of the circulation, and this is likely to impact on the accuracy of the predictions of surgical outcome and recovery.

Objectives: This project aims to improve predictions of post-operative outcome by modelling acute and chronic effects of liver surgery on blood circulation.

Novelty: Current risk assessment of liver surgery uses inaccurate methods derived from unspecific blood biomarkers, leaving some patients exposed to significant risk or unnecessarily excluded from potentially life-saving surgery. The approach developed in this study will provide an innovative and more accurate patient-specific solution to surgical risk assessment. The study benefits from the rich dataset provided by the HEPARIM study, which offers unique functional and anatomical data that were not previously available to inform model development.

Timeliness: Liver cancer affects 6,200 new patients in the UK every year with one of the lowest survival rates. Incidence rates have increased by 166% in the last two decades, and the demand for liver-sparing resection has grown considerably. This project will address the urgent need for better risk assessment to ultimately offer this treatment option to more patients and reduce incidence of post-surgery complications.

Experimental approach: We will first model the acute effect of liver resection on blood flow using computational fluid dynamics (CFD) on an image-segmentation of the liver circulation. This patient-specific model will be coupled to an existing whole-circulation model (openBF) to estimate secondary compensatory effects and their impact on the liver. Modelling will be validated in vitro using particle image velocimetry on a patient-specific silicone model, and guided by a combination of anatomical and functional MRI datasets from the HEPARIM study. The clinical potential will be tested by comparing model-based predictions against post-operative outcome measures in a separate HEPARIM dataset.

Training: Dr Marzo and his research group will offer training in cardiovascular modelling, machine learning methods, VVUQ (Verification, Validation and Uncertainty Quantification), statistical methods, and experimental analysis through Particle Image Velocimetry (PIV). Prof. Sourbron will offer training in advanced MRI image analysis, including acquisition, image processing, modelling of quantitative MRI and quality assurance in clinical trials. Mr Prasad (Consultant Hepatobiliary & Transplant Surgeon) will offer training in liver anatomy, physiology, and pathology with a focus on hepatic surgery and its clinical management.

Further information can be found through links below:

https://www.sheffield.ac.uk/mecheng/academic-staff/alberto-marzo

https://www.sheffield.ac.uk/medicine/people/iicd/steven-sourbron

https://www.sheffield.ac.uk/insigneo

Candidate: We are looking for highly-motivated candidates with strong technical skills in programming (Python, C, C++ or equivalent) and, ideally, knowledge of fluid mechanics and computational fluid dynamics (ANSYS-Fluent/CFX, openFOAM), with an interest in doing research at the interface between engineering and medicine.

The student will have the opportunity to engage with an industrial partner, Perspectum Ltd, with specialised expertise in translating MRI-based approaches to the clinic for the treatment of liver tumours. Hepatica® is one of their recent liver cancer surgery products developed from prototype into a regulatory cleared medical device. The expectation is that these advanced methods will produce a prototype liver surgery simulation tool with potential to impact on clinical practice in the short term. The company will contribute with software and training for processing of MRI datasets, feedback on project outcomes, and will offer the student a 3-months period spent in its premises in Oxford (UK) covering all associated costs (travel, accommodation).

Benefits of being in the DiMeN DTP:

This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle, York and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.

We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.

Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: https://www.dimen.org.uk/blog

Further information on the programme and how to apply can be found on our website:

https://www.dimen.org.uk/how-to-apply