Brain metastasis affect an estimated 20% of cancer patients. Current treatment typically involves surgery, external beam radiation or both. Stereotactic radiotherapy (SRT) is a local therapy that can offer improved survival and reduced morbidity for cancer patients while avoiding whole-brain radiotherapy, which causes cognitive impairment [1]. However, -predicting brain metastasis (BM) patient response to SRT treatment on an individual basis remains elusive, while no digital tools for clinical decision-making are available in the clinical routine. In this contribution, we present an in silico model of BM dynamics in response to SRT that is designed to incorporate key biological processes at the organ-scale: enhancing tumour region, oedema development and brain tissue necrosis. The model is formulated as a system of partial differential equations representing the spatio-temporal dynamics of each component and their interactions. It also integrates patient-specific structural data (patient’s brain geometry), radiation dose distribution maps and the tumour region, to provide a personalized framework for predicting disease progression and treatment efficacy. The in silico model is applied to a retrospective cohort of 40 BM patients in Cyprus [2], and is calibrated against the actual regions of tumour/necrosis/oedema obtained from imaging data at two to five time points per patient. Our results demonstrate the model's capability to replicate the spatio-temporal evolution of metastatic brain tumours, including tumour growth, necrosis and oedema development. We have also explored the assimilation of patient data towards predicting the evolution of the tumour enhancing region using input from only one or two follow-ups post SRT. In this talk, we will demonstrate the ability of our in silico model to predict BM treatment outcomes, and we will discuss about the potential of the data assimilation technique for clinical application and future perspectives towards external validation and translation of our in silico modelling approach. [1] Kuntz et al. 2023, Radiat Oncol. https://doi.org/10.1186/s13014-022-02194-0 [2] Flouri et al. 2025, Sci Data. https://doi.org/10.1038/s41597-025-06131-0