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The overall aim of this project is to explore the application of machine learning techniques to develop the underpinnings of clinical tools for planning and evaluating minimally-invasive tumour ablation as a treatment for cancer in the liver, kidney, and other abdominal organs. In particular, the project student will analyse retrospective imaging data collected before and after ablative procedures with a view to identifying and learning relationships between the characteristics of the ablated tissue region and the incidence of short-term and long-term treatment outcomes and treatment-related complications. Where a clear causal relationships are established, the focus will move to determining which treatment parameters, such as tumour location, the size of treatment margin, dose distribution, etc., are associated with treatment success or failure, and using this information to develop an algorithm to predict the optimal treatment parameters for the individual patient given input image data (with additional diagnostic data as appropriate). In terms of methodology, the project will draw upon state-of-the-art methods in machine learning, combined with established and novel medical image analysis and computational modelling techniques (for instance, biophysical modelling of thermal tumour ablation).
Supervisors
Dr Dean Barratt
Dr Steve Bandula
Dr Yipeng Hu