Brain metastases are a common and serious complication among cancer patients and are often treated by stereotactic radiosurgery, such as Gamma Knife therapy. While this treatment does an excellent job of controlling the local tumor, it is necessary to discriminate between stable and recurrent disease. Existing evaluation strategies are largely based on expert visual interpretation of serial MRI and Radiotherapy-Planning Images; however, this approach requires intensive manual handling of Radiotherapy Dose data and, as such, introduces variability across operators and observers. This thesis aims to evaluate the current literature on the topic and introduces an effective deep-learning architecture designed to classify brain lesions as stable or recurrent after Gamma Knife radiosurgery.