Digital Twins for Liver Cancer Using Medically Informed Machine Learning - EPSRC Doctoral Landscape Award

This PhD project develops digital twin models for the human liver for use in liver cancer treatment planning and optimisation. The research focuses on creating an image-based computational model (digital twin) of the liver with realistic anatomical variability, structure, and functionality. The project exploits multi-modal imaging (CT, MRI) and novel data-driven machine learning methods to develop and validate the digital twin model. The research addresses Hepatocellular carcinoma (HCC), the most common type of primary liver cancer in adults. The project aims to facilitate the design of novel radiotherapy interventions by developing a liver function map as part of a digital twin model. Biomarkers from pre-operative MRI will be used to estimate tissue-level inflammation, fibrosis, fat content, cirrhosis, and extraction rate using medically informed machine learning (MIML). This is a competitive EPSRC Doctoral Landscape Award providing full academic fees and a tax-free maintenance grant for 3.5 years. The project is supervised by Dr Sharib Ali and Dr Toni Lassila at the School of Computer Science, with additional supervision from Professor Adel Samson from Medicine & Health.