K. R. Swanson

This paper develops BioNet, a biologically-informed deep learning model, to predict intratumoral heterogeneity in post-treatment glioblastoma using MRI and biopsy data, showing improved performance over existing methods.

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

This paper uses mathematical modeling to differentiate between drug insensitivity and poor drug delivery in glioblastoma PDX models, validated by experimental results.

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.

This study extends a GBM mechanistic model to investigate how ischemia influences distal tumor recurrence by incorporating vascular efficiency and hypoxia.

This study uses a computational model to show that hypoxic cell migration drives glioblastoma growth and distant recurrence.

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

The paper extends the PIHNA model to study how hypoxic and normoxic cell migration rates affect glioblastoma growth wave-speed, identifying a minimum wave-speed and conditions for increased growth.

The paper develops a mathematical model to quantify glioblastoma drug response dynamics, finding that BBB permeability may slightly outweigh drug resistance in therapeutic efficacy.

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 paper develops a hybrid agent-based model to study how intratumor heterogeneity affects glioblastoma growth and treatment response using multiscale data and mathematical frameworks.

The paper models therapeutic response in glioblastoma using bioluminescence imaging data to assess drug resistance and penetration.

The paper uses image-based metrics to predict TMZ response in GBM patients, finding that less invasive tumors show greater volumetric response and improved survival.

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