MRI of brain metastases in the era of immunotherapy, targeted agents, and artificial intelligence: from detection to treatment response assessment.
Authors
Affiliations (6)
Affiliations (6)
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins School of Medicine, Baltimore, USA.
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
- Department of Oncology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, Johns Hopkins School of Medicine, Baltimore, USA. [email protected].
Abstract
Magnetic resonance imaging (MRI) is central to the detection, treatment planning, and response assessment of brain metastases (BM). However, advances in stereotactic radiosurgery, immunotherapy, and targeted therapy have substantially altered the imaging appearance and treatment response patterns of BM, which are not fully captured by conventional imaging and conventional size-based response criteria. This review discusses the evolving role of MRI in BM, from optimized lesion detection to treatment response assessment. We review advanced contrast-enhanced MRI techniques, including three-dimensional turbo spin echo black-blood imaging and post-contrast T2-weighted FLAIR, as well as recent developments in artificial intelligence (AI)-assisted detection. We also examine limitations of RANO-BM and discuss the emerging role of AI-based volumetric analysis for quantitative longitudinal assessment. For differentiating radiation necrosis from tumor progression, we review diffusion and perfusion MRI, MR spectroscopy, and emerging approaches including chemical exchange saturation transfer imaging, radiomics, and tumor habitat analysis. Finally, we highlight how the integration of optimized MRI protocols, multi-parametric imaging, and validated AI-based quantitative tools may improve treatment response assessment and support more precise clinical decision-making for patients with BM.