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This paper proposes Observation-Anchored Selective Assimilation (OASA) for forecasting longitudinal tumor-state proxies in glioma patients using MRI data, improving prediction accuracy over baseline methods.
GLI-AL is a new label resource for glioma MRI that unifies anatomy and lesion labels, expanding supervision to include healthy tissues and previously unlabeled abnormalities. It provides 1,251 label sets aligned with BraTS-GLI cases.
This paper introduces a residualization-and-permutation diagnostic to separate predictability-driven from regulation-driven variance in regulatory importance scores from genomic foundation models, applied to dark genome elements at glioma-relevant loci.