Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth

Baoshan Liang, J. Tan, Luke Lozenski, D. Hormuth, T. Yankeelov, Umberto Villa, D. Faghihi

ArXiv, 2022

A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data

Junyan Liu, D. Hormuth, Jianchen Yang, T. Yankeelov

Frontiers in Oncology, 2022

Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging

B. Tunç, David A. Hormuth II, G. Biros, T. Yankeelov

IEEE Transactions on Biomedical Engineering, 2021

Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy

D. Hormuth, Angela M. Jarrett, Tessa Davis, T. Yankeelov

Cancers, 2021

A time-resolved experimental-mathematical model for predicting the response of glioma cells to single-dose radiation therapy.

Junyan Liu, D. Hormuth, Tessa Davis, Jiancheng Yang, M. McKenna, Angela M. Jarrett, H. Enderling, A. Brock, T. Yankeelov

Integrative Biology, 2021

Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling

D. Hormuth, Angela M. Jarrett, T. Yankeelov

Radiation Oncology, 2020

Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI

D. Hormuth, Angela M. Jarrett, Xinzeng Feng, T. Yankeelov

Annals of Biomedical Engineering, 2019

Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details.

D. Hormuth, Stephanie L. Eldridge, J. Weis, M. Miga, T. Yankeelov

Methods in molecular biology, 2018