A. Hawkins-Daarud

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

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.

Mapping multiregional imaging-localized biopsies to MRI habitats reveals biologically significant glioma tissue states and associated patterns of cohabitating cellular subpopulations

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

Complementary role of mathematical modeling in preclinical glioblastoma: differentiating poor drug delivery from drug insensitivity

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

A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma

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

Modeling the Role of Hypoxia in Glioblastoma Growth and Recurrence Patterns

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

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

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

Sex Differences in Predictors of Seizure in Contrast-Enhancing Gliomas at Clinical Presentation: A Network Approach

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.

Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

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.

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: a Multi-disciplinary Team-based, Multifactorial Analytical Approach

The paper introduces the ENDURES consortium to study extreme survivors of glioblastoma by integrating clinical data and creating patient-specific tumor models.

Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients

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