Advanced medical imaging in oncology is increasingly recognized for its value in radiotherapy guidance and treatment personalization. Multimodal imaging datasets acquired throughout the radiotherapy workflow can yield image-based biomarkers with strong predictive power. Yet, current clinical models for tailoring treatment dosimetry still exclude patient-specific imaging information, relying instead on parameters derived from in-vitro studies and thereby overlooking the in-vivo biological complexity of each patient. This limitation becomes even more pronounced in particle therapy, where the radiobiological advantages of this advanced modality remain largely underexploited on a patient-specific basis.

This project aims to advance PT treatments by integrating imaging biomarkers derived from quantitative Magnetic Resonance Imaging (MRI) into personalized PT, by implementing multi-scale models able to characterize tumour microstructure and its interactions with the radiation beam.

First, advanced computational models will be developed to extract macro- and micro-scale imaging biomarkers from quantitative MRI data, capturing tissue characteristics both at the voxel level and at sub-voxel resolution. These biomarkers will then be integrated into multi-scale modelling frameworks, including statistical methods, machine learning approaches, and Monte Carlo simulations. The capability of these models to characterize tumours and predict treatment outcomes in PT will be assessed using clinical parameters and biological analyses. Finally, the project will translate these findings into personalized treatment plans tailored to the biological profile of each individual tumour.