Radiotherapy is a fundamental modality in oncology treatment, typically delivered over multiple fractions across several weeks. Throughout the treatment course, patients may exhibit substantial anatomical modifications (such as tumour regression or weight loss) as well as biological and functional changes within both malignant and healthy tissues. These dynamic variations can compromise the precision and therapeutic efficacy of radiation delivery, making the initial treatment plan suboptimal and necessitating adaptive strategies to maintain clinical outcomes.

Current treatment planning and adaptation strategies in radiotherapy predominantly focus on the physical dose distribution delivered to the patient. While effective to some extent, these approaches offer limited personalization and frequently neglect individual biological variability, such as tumour response dynamics and normal tissue sensitivity.

This project seeks to enhance both the efficacy and personalization of radiotherapy by integrating biological modelling with conventional dose-based planning. Specifically, it will focus on developing methodologies that leverage biological information and imaging biomarkers for optimizing the initial treatment plan and guide adaptive strategies throughout the treatment course.

The project has three main components:

1. Robust radiobiological optimization – Improving initial treatment planning by considering not only dose but also radiobiological models that predicts how tumours and healthy tissues respond to radiation, while accounting for underlying uncertainties.
2. Functional imaging integration – Using advanced imaging (e.g., PET/fMRI combined with CT or other anatomical imaging) to monitor both anatomical changes and tissue responses, enabling adaptation of the radiotherapy treatment based on both anatomical and biological indicators.
3. Biologically guided adaptive planning – Combining these approaches into a research prototype of a commercial treatment planning system for individualised initial treatment planning and adaptive treatments, accounting for underlying geometrical and biological uncertainties.

By incorporating biological and imaging data, this project aims to enable more personalized and adaptive radiotherapy treatments, moving beyond traditional dose-based methods.