Personalising radiotherapy for lung cancer through mathematical modelling

Project Outline

Radiotherapy for non-small cell lung cancer (NSCLC) is still largely one-size-fits-all — dose and fractionation are set by tumour stage and performance status, despite variation in how individual tumours actually behave. This PhD aims to address this. 

The successful student will build and validate a mathematical model of tumour response based on the Proliferation Saturation Index (PSI) — a simple, patient-specific measure of how close a tumour sits to its growth ceiling, estimated directly from routine pre-treatment imaging. Unlike many tumour models, PSI uses a single identifiable parameter, making it a realistic candidate for real clinical translation. 

Working with a dataset of 500+ Manchester patients plus international validation cohorts from Dublin, Belfast, and Toronto, you will: 

• Test and refine the model across different radiotherapy schedules. 

• Extend it to capture the effects of chemotherapy and immunotherapy, including immune-related response patterns. 

• Link model predictions to real clinical outcomes — local tumour control and survival, not just image-based tumour regression. 

• Build a virtual trial framework to simulate and test personalised treatment strategies computationally, ahead of future clinical trials. 

This project sits at the interface of mathematical oncology, medical physics, and clinical radiotherapy, embedded within the Manchester Cancer Research Centre. You’ll work alongside clinicians, medical physicists, and radiobiologists, gaining hands-on experience in computational modelling, medical image analysis, and survival statistics — skills in high demand across academic and clinical research careers. 

This is a rare opportunity to contribute to a project with a genuine, near-term translational pathway: PSI-guided treatment selection is already progressing toward clinical trials in head and neck cancer, and this project lays the groundwork for the same in lung cancer. 

Applications for this project are now open. Please complete your application on The University of Manchester website.