Longitudinal assessment of radiosurgery response in small brain metastases: AI‐driven precision tumor segmentation and monitoring on serial MRI

BACKGROUND: The conventional method of assessing radiotherapy outcome in brain metastases (BM) is based on monitoring tumor size alterations on serial magnetic resonance imaging (MRI). To accurately determine changes in tumor dimensions, targets require delineations on several volumetric images acquired before treatment and at multiple follow-up scans after radiotherapy. However, manual tumor delineation on serial MRI is labor-intensive, imposes a significant burden on the clinical workflow, and is prone to variability especially for smaller lesions. PURPOSE: This study proposes a novel multi-step transformer-based automated framework with a 3D neighborhood attention mechanism, specifically designed to enhance the segmentation precision for BM of various sizes on standard longitudinal MRI. This framework leverages the hierarchical encoding capabilities of transformer architecture to capture intricate tumor characteristics, with a particular focus on improving the delineation of small metastases (