This session surveys state-of-the-art MRI radiomics and deep learning methods that extract quantitative imaging biomarkers from diverse MRI contrasts (diffusion-weighted MRI, multi-parametric MRI, ASL perfusion, UTE-MRI, and high-resolution MRSI) to support precision diagnosis, molecular characterization, risk stratification, and treatment response prediction. Use cases span neuro-oncology (IDH status, glioblastoma heterogeneity), neurodegeneration (Alzheimer’s/non-AD dementia), vascular imaging (symptomatic carotid plaques), head & neck oncology (parotid tumors), gynecologic oncology (post-CCRT survival in cervical cancer), rheumatology (axial spondyloarthritis response), and bone health (osteoporotic microstructural deterioration). Emphasis is placed on interpretability/explainability, multicenter generalizability, and emerging paradigms such as federated learning and spatial statistics–driven radiomics.

Skill Level: Intermediate