A new machine learning tool is transforming decision-making in stereotactic radiosurgery (SRS) for small brain metastases. The tool aims to evaluate factors such as radiation dose, patient characteristics, and treatment-related factors to determine the likelihood of local failure at 6 months, 1 year, and 2 years post-treatment.1
Brain metastases, especially those that are under 2 cm, present challenges in achieving optimal local control following SRS. Conventional treatment dosing typically consists of 20 Gy, 22 Gy, or 24 Gy, and relies on general guidelines. However, these fail to account for nuanced patient-specific factors.
By integrating AI into the decision-making process, this machine learning model offers clinicians the ability to assess local failure probabilities at 6 months, 1 year, and 2 years…