Digital Twins for Liver Cancer Using Medically Informed Machine Learning

The project develops digital twin models for the human liver for use in liver cancer treatment planning and optimisation. It develops an image-based computational model (called a digital twin) of the liver with realistic anatomical variability, structure, and even some aspects of functionality. Digital twins have many use cases, such as training clinicians, testing computational algorithms, planning patient-specific treatments, or enabling virtual in-silico trials for evaluating novel treatments. The project seeks to exploit multi-modal imaging (CT, MRI) and novel data-driven machine learning methods to develop and validate the digital twin model.

<p><strong>Background</strong></p> <p>Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults and is currently the most common cause of death in people with cirrhosis of the liver. The 5-year relative survival rate is 21%. One treatment for HCC that can’t be treated by surgical recission (removal of tissue) is Stereotactic Ablative Radiotherapy (SABR), a precise form of radiotherapy that delivers high doses of radiation to a tumour from many different angles, minimising damage to surrounding healthy tissue.  Recent evidence indicates that the treatment toxicity could be reduced by delivering lower dosages to high functioning liver tissue, but standard clinical imaging methods do not provide enough information about tissue function to achieve this.  </p> <p><strong>Aims</strong></p> <p>To facilitate the design of novel radiotherapy interventions, a liver function map will be developed as part of a digital twin model for the human liver [1]. Biomarkers from pre-operative MRI will be used to estimate tissue-level inflammation, fibrosis, fat content, cirrhosis, and extraction rate of the hepatocyte uptake using a combination of magnetic resonance imaging and medically informed machine learning [2]. The liver function maps will be paired with anatomical shape models [3,4] of the liver and computational algorithms for generating the vasculature of the liver and the tumour, to form the first ever digital twin model of the human liver. </p> <p>Medically informed machine learning (MIML) refers to a broad category of techniques [2] used to incorporate medical, biological, and anatomical information as constraints to machine learning. In this project, MIML will be used to reduce model overfitting and to ensure that the digital twin liver consistent with existing medical knowledge about tumour pathophysiology, vascular function, and tissue response to radiotherapy. The project will also seek to develop new MIML techniques where possible.</p> <p><strong>References</strong></p> <p>[1] Zhou Z, Zakeri A, Dou H, Xue Y, Macraild M, Huang J, Lin F, Sarrami-Foroushani A, Duan J, Frangi AF. Synthetic Anatomy: Deep Learning Models for Virtual Population Generation: A Review. medRxiv. 2025:2025-10. </p> <p>[2] Leiser F, Rank S, Schmidt-Kraepelin M, Thiebes S, Sunyaev A. Medical informed machine learning: A scoping review and future research directions. Artificial Intelligence in Medicine. 2023 Nov 1;145:102676.<br /> [3] Lorente S, Hautefeuille M, Sanchez-Cedillo A. The liver, a functionalized vascular structure. Scientific Reports. 2020 Oct 1;10(1):16194.<br /> [4] Lu YC, Kemper AR, Gayzik S, Untaroiu CD, Beillas P. Statistical modeling of human liver incorporating the variations in shape, size, and material properties. SAE Technical Paper; 2013 Nov 11. </p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>EPSRC DTP Engineering & Physical Sciences</strong></em> (if you do not apply under this programme code, your application will not be considered), in the research information section that the research degree you wish to be considered for is <em><strong>Digital Twins for Liver Cancer Using Medically Informed Machine Learning</strong></em> as well as <a href="https://eps.leeds.ac.uk/computing/staff/1542/dr-toni-lassila">Dr Toni Lassila</a> as your proposed supervisor and that the funding you wish to be considered for is <em><strong>EPSRC Doctoral Landscape Award 2026/27: Computer Science.</strong></em></p> <p>Applications for the EPSRC Doctoral Landscape Award will be considered after the closing date of Friday 30 January 2026.  Potential applicants are strongly encouraged to contact supervisors for an informal discussion before making a formal application. We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p><strong>Please note that you must provide the following documents in support of your application by the closing date of Friday 30 January 2026 for the EPSRC Doctoral Landscape Award:</strong></p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date including grading scheme</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul> <p><strong>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</strong></p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.5 overall with at least 6.5 in writing and at least 6.0 in reading, listening and speaking or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<p>A highly competitive EPSRC Doctoral Landscape Award, providing full academic fees, together with a tax-free maintenance grant at the standard UKRI rate (£20,780 in academic session 2025/26) for 3.5 years.  Training and support will also be provided.<br /> <br /> This opportunity is open to UK applicants only.  All candidates will be placed into the EPSRC Doctoral Landscape Award Competition and selection is based on academic merit.</p> <p>Please note that there are only 2 funded places available to UK applicants only and this project is in competition with several other projects to secure this funding.  If you are successful in securing an academic offer for PhD study, this does not mean that you have been successful in securing an offer of funding.</p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p> <p>For further information about this project, please contact Dr Toni Lassila by email to <a href="mailto:T.Lassila@leeds.ac.uk">T.Lassila@leeds.ac.uk</a></p>