Personalised Survival Prediction and Treatment Optimisation in Oesophageal Cancer

About the project

About the Project Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project. The PhD will be based in the School of Electrical and Mechanical Engineering and will be supervised by Dr Shamsul Masum , Dr Edward Smart and Philip Pucher (Consultant from Portsmouth Hospital University Trust). Oesophageal cancer presents major clinical uncertainties, particularly regarding which patients benefit most from endoscopic therapy versus surgical resection. Despite advances in minimally invasive treatments, decision-making remains inconsistent due to heterogeneous tumour biology, variable comorbidity profiles, and incomplete prognostic tools. This PhD project aims to develop a personalised survival prediction and treatment recommendation framework using advanced survival models, machine learning techniques, and real-world clinical data. By integrating endoscopic, pathological, demographic, and treatment variables, the project will produce models capable of predicting individual survival trajectories and estimating the expected benefit of different treatment strategies. The ultimate goal is to create a clinically deployable decision-support tool , promoting precision management and improving long-term outcomes for patients with oesophageal cancer. The work on this project will: Development of individualised survival prediction models (Cox models, machine learning survival models, ensemble methods). Use of a real-world clinical dataset including tumour stage, LVI, differentiation, comorbidity, Barrett’s status, and treatment variables. Simulation of patient-specific treatment strategies , including endoscopic treatment versus surgical treatment. Estimation of absolute survival benefits at 1, 2, 3, and 5 years for competing treatment options. Construction of a prototype clinical decision-support system for personalised cancer management. Project description Oesophageal cancer remains one of the most lethal gastrointestinal malignancies, yet early-stage disease offers an important therapeutic window where outcomes can be significantly improved through optimal treatment selection. Current guidelines provide general recommendations but lack mechanisms to deliver individualised predictions tailored to patient comorbidities, tumour characteristics, and local treatment pathways. This PhD project seeks to address this evidence gap by developing a robust, interpretable, and clinically useful personalised survival prediction and treatment optimisation system for oesophageal cancer. The project will build upon a high-quality dataset from Portsmouth University Hospital Trust, patients treated with endoscopic resection or surgery, incorporating variables such as age, gender, Charlson comorbidity index, tumour differentiation, lymphovascular invasion, resection margin status, tumour site, and pathological T stage. Traditional Cox proportional hazards models will be combined with advanced machine-learning survival approaches (e.g., random survival forests, gradient-boosted survival models, and deep learning–based survival networks) to generate accurate patient-specific survival curves. Model performance will be rigorously validated using cross-validation, calibration analyses, and external testing where possible. A core innovation of the project is the development of a treatment simulation engine capable of estimating how an individual patient's survival trajectory would change under different management strategies. By quantifying absolute and relative risk reductions, this tool will allow clinicians to compare expected outcomes from endoscopic versus surgical resection for each patient. The final output will be a prototype clinical decision-support system that integrates statistical and ML predictions into an interpretable, user-friendly interface suitable for multi-disciplinary team discussions and patient counselling. Beyond methodological development, the student will contribute to research publications, conference abstracts, and potential integration of the tool into clinical workflows. This PhD offers a unique opportunity to shape the future of personalised management in early oesophageal cancer, combining computational modelling with direct translational clinical relevance. General admissions criteria You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject (Computer Science, Data science, Data analytics, Artificial intelligence, Machine Learning) or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. To make the requirements more open and inclusive, applicants who have completed a substantial project, dissertation, or professional work in a related technical area may also be considered, even if their formal degree is not directly aligned with the fields listed above. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0. International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office. How to Apply We’d encourage you to contact Dr Shamsul Masum ( shamsul.masum@port.ac.uk to discuss your interest before you apply, quoting the project code. When you are ready to apply, please follow the ' Apply now ' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘ How to Apply ’ page offers further guidance on the PhD application process. When applying please quote project code: SEM10460526

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