As the radiotherapeutic management of brain metastases increasingly utilizes stereotactic radiosurgery (SRS) and repeated treatments, artificial intelligence (AI) applications are being investigated in treatment planning, prognostication, and evaluation of treatment effects versus tumor progression. The burden on radiation oncologists increases as more lesions are targeted with SRS. AI algorithms facilitate improved detection and segmentation of lesions, reduce interobserver variability, and save clinician time. Predictive analytics, based on large datasets, enable better prognostication and treatment strategies tailored to individual patients. There is also data for the differentiation between radiation necrosis and tumor progression, which is a difficult issue that comes up more and more in patient care. However, challenges remain regarding data standardization, model validation, and clinical integration. Continued research and interdisciplinary collaboration are essential to fully harness AI's potential in the radiotherapeutic management of brain metastases and improving patient outcomes in neuro-oncology.
Podgorsak et al. (Fri,) studied this question.