Radiomics and Clinical Features in Predictive Modelling of Brain Metastases Recurrence

Brain metastases affect approximately between 20% and 40% of cancer patients and are commonly treated with radiotherapy or radiosurgery. Early prediction of recurrence following treatment could enable timely clinical intervention and improve patient outcomes. This study proposes an artificial intelligence based approach for predicting brain metastasis recurrence using multimodal imaging and clinical data. A retrospective cohort of 97 patients was collected, including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) acquired before treatment and at first follow-up, together with relevant clinical variables. Image preprocessing included CT windowing and artifact reduction, MRI enhancement, and multimodal CT MRI registration. After applying inclusion criteria, 53 patients were retained for analysis. Radiomics features were extracted from the imaging data, and delta radiomics was employed to characterize temporal changes between pre-treatment and follow-up scans. Multiple machine learning classifiers were trained and evaluated, including an analysis of discrepancies between treatment planning target volumes and delivered isodose volumes. Despite limitations related to sample size and class imbalance, the results demonstrate the feasibility of radiomics based models, namely ensemble models, for recurrence prediction and suggest a potential association between radiation dose discrepancies and recurrence risk. This work supports further investigation of AI-driven tools to assist clinical decision-making in brain metastasis management.