L. S. Hu

This paper uses graph-based modeling of multiparametric MRI to identify molecular states in high-grade glioma and shows their prognostic significance.

The paper maps MRI-localized glioma biopsies to MRI habitats, identifying transcriptional tissue states and their association with cellular subpopulations and MRI features.

This study examines sex differences in MRI and MRE metrics related to glioma invasion and brain mechanics, finding sex-dependent relationships between tumor stiffness and growth parameters.

The paper uses biopsy data and a mechanistic model to study interactions between EGFR and PDGFRA amplified sub-populations in glioblastomas.

This study evaluates Days Gained (DG) as a metric for assessing treatment response in recurrent glioblastoma patients receiving bevacizumab-based therapies, showing its prognostic significance for survival outcomes.

This study examines the association between tumor location and seizure risk in male and female glioma patients.

The paper uses a machine learning model to predict seizure risk in glioma patients at presentation, finding sex, tumor size, and age as significant predictors.

This study identifies sex-specific predictors of survival in glioblastoma patients using MR imaging and clinical data.