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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

Abstract 2742: A biology-based, mathematical model to predict the response of recurrent glioblastoma to treatment with 186Re-labeled nanoliposomes

Chase J. Christenson, Chengyue Wu, D. Hormuth, Shiliang Huang, A. Brenner, T. Yankeelov

Cancer Research, 2022

Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-guided fluid dynamics

Chengyue Wu, D. Hormuth, G. Lorenzo, Angela M. Jarrett, F. Pineda, Frederick M. Howard, G. Karczmar, T. Yankeelov

IEEE Transactions on Biomedical Engineering, 2022

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Chengyue Wu, G. Lorenzo, D. Hormuth, E. Lima, Kalina P. Slavkova, J. DiCarlo, J. Virostko, Caleb M. Phillips, D. Patt, C. Chung, T. Yankeelov

Biophysical Reviews, 2022

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy.

D. Hormuth, Maguy Farhat, Chase J. Christenson, Brandon Curl, C. Chad Quarles, C. Chung, T. Yankeelov

Advanced Drug Delivery Reviews, 2022

A generalized framework for model

Inês G. Gonçalves, D. Hormuth, Sandhya Prabhakaran, M. Caleb, Phillips

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

NIMG-79. SPATIALLY MAPPED PREDICTIONS OF EVOLVING TUMOR RESPONSE OF HIGH-GRADE GLIOMA VIA IMAGE-DRIVEN MATHEMATICAL MODELING

Maguy Farhat, D. Hormuth, Holly Langshaw, Juliana Bronk, Brandon Curl, D. Yadav, R. Upadhyay, A. Elliot, Jodi Goldman, Lily G. Erickson, Wasif Talpur, Maggie Lee, T. Yankeelov, Caroline Chung

Neuro-Oncology, 2022

Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.

Angela M. Jarrett, Anum S Kazerouni, Chengyue Wu, J. Virostko, A. Sorace, J. DiCarlo, D. Hormuth, David A. Ekrut, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

Nature Protocols, 2021

Abstract PS13-18: Predicting breast cancer response to neoadjuvant therapies using a mathematical model individualized with patient-specific magnetic resonance imaging data: Preliminary Results

Angela M. Jarrett, D. Hormuth, A. Syed, Chengyue Wu, J. Virostko, A. Sorace, J. DiCarlo, J. Kowalski, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

2021

Abstract 3138: Identification of therapy-sensitive tumor phenotypes using quantitative MRI habitats in a preclinical model of HER2+ breast cancer

Anum S Kazerouni, D. Hormuth, Tessa Davis, Meghan J. Bloom, J. Virostko, T. Yankeelov, A. Sorace

Tumor Biology, 2021

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

RADT-14. TOWARDS IMAGE-GUIDED MODELING OF PATIENT-SPECIFIC RHENIUM-186 NANOLIPOSOME DISTRIBUTION VIA CONVECTION-ENHANCED DELIVERY FOR GLIOBLASTOMA MULTIFORME

Chengyue Wu, D. Hormuth, Chase J. Christenson, M. Abdelmalik, W. Phillips, Thomas Hughes, A. Brenner, T. Yankeelov

Neuro-Oncology, 2021

Abstract 222: Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-based fluid dynamics

Chengyue Wu, D. Hormuth, F. Pineda, G. Karczmar, T. Yankeelov

Bioinformatics and Systems Biology, 2021

An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom

Chengyue Wu, D. Hormuth, T. Easley, V. Eijkhout, F. Pineda, G. Karczmar, T. Yankeelov

Medical Image Anal., 2021

Math, magnets, and medicine: enabling personalized oncology

D. Hormuth, Angela M. Jarrett, G. Lorenzo, E. Lima, Chengyue Wu, C. Chung, D. Patt, T. Yankeelov

Expert Review of Precision Medicine and Drug Development, 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

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

D. Hormuth, Caleb M. Phillips, Chengyue Wu, E. Lima, G. Lorenzo, P. Jha, Angela M. Jarrett, J. Oden, T. Yankeelov

Cancers, 2021

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

D. Hormuth, K. A. Al Feghali, A. Elliott, T. Yankeelov, C. Chung

Scientific Reports, 2021

Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

G. Lorenzo, D. Hormuth, Angela M. Jarrett, E. Lima, Shashank Subramanian, G. Biros, J. Oden, T. Hughes, T. Yankeelov

ArXiv, 2021

Abstract PO-091: Modeling the effects of glucose accessibility on tumor cell growth: A comparison of mechanism-based and machine learning models

Jiancheng Yang, J. Virostko, D. Hormuth, T. Yankeelov

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

Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI.

Martin D Holland, Andrés Morales, Sean Simmons, Brandon Smith, S. Misko, Xiaoyu Jiang, D. Hormuth, Chase J. Christenson, R. Koomullil, D. Morgan, Yufeng Li, Junzhong Xu, T. Yankeelov, Harrison Kim

Medical physics, 2021

Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme

Ryan T Woodall, David A. Hormuth II, Chengyue Wu, M. Abdelmalik, W. Phillips, A. Bao, T. Hughes, A. Brenner, T. Yankeelov

Biomedical engineering and physics express, 2021

Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness

S. McGarry, M. Brehler, J. Bukowy, A. Lowman, S. Bobholz, S. Duenweg, A. Banerjee, S.L. Hurrell, D. Malyarenko, T. Chenevert, Yue Cao, Yuan Li, D. You, A. Fedorov, L. Bell, C. Quarles, M. Prah, K. Schmainda, B. Taouli, E. LoCastro, Y. Mazaheri, A. Shukla-Dave, T. Yankeelov, D. Hormuth, A. Madhuranthakam, K. Hulsey, Kurt Li, Wei Huang, Wei Huang, M. Muzi, M. Jacobs, M. Solaiyappan, S. Hectors, T. Antic, G. Paner, Watchareepohn Palangmonthip, K. Jacobsohn, M. Hohenwalter, Petar Duvnjak, Michael Griffin, W. See, M. Nevalainen, K. Iczkowski, P. LaViolette

Journal of magnetic resonance imaging : JMRI, 2021

Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities

Angela M. Jarrett, D. Faghihi, D. Hormuth, E. Lima, J. Virostko, G. Biros, D. Patt, T. Yankeelov

Journal of Clinical Medicine, 2020

Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data12

Angela M. Jarrett, D. Hormuth, Chengyue Wu, Anum S Kazerouni, David A. Ekrut, J. Virostko, A. Sorace, J. DiCarlo, J. Kowalski, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

Neoplasia, 2020

Abstract P2-16-17: Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results

Angela M. Jarrett, D. Hormuth, Chengyue Wu, J. Virostko, A. Sorace, J. DiCarlo, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

2020

Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

Angela M. Jarrett, D. Hormuth, V. Adhikarla, P. Sahoo, D. Abler, L. Tumyan, D. Schmolze, J. Mortimer, R. Rockne, T. Yankeelov

medRxiv, 2020

Abstract 5485: Patient-specific neoadjuvant regimens for breast cancer identifiedviaimage-driven mathematical modeling

Angela M. Jarrett, E. Lima, D. Hormuth, Chengyue Wu, J. Virostko, A. Sorace, J. DiCarlo, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

2020

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

Anum S Kazerouni, M. Gadde, Andrea L Gardner, D. Hormuth, Angela M. Jarrett, Kaitlyn E. Johnson, E. Lima, G. Lorenzo, Caleb M. Phillips, A. Brock, T. Yankeelov

iScience, 2020

Abstract 4263: Characterization of patient-specific drug delivery for breast cancer using image-guided computational fluid dynamics

Chengyue Wu, D. Hormuth, F. Pineda, G. Karczmar, R. Moser, T. Yankeelov

2020

Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics

Chengyue Wu, D. Hormuth, Todd A. Oliver, F. Pineda, G. Lorenzo, G. Karczmar, R. Moser, T. Yankeelov

IEEE Transactions on Medical Imaging, 2020

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

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

Radiation Oncology, 2020

Abstract 5487: A biologically-motivated mathematical model for forecasting chemoradiationresponse in high grade gliomas: Initial results

D. Hormuth, K. Feghali, A. Elliot, C. Chung, T. Yankeelov

2020

An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines

Jiancheng Yang, J. Virostko, D. Hormuth, Junyan Liu, A. Brock, J. Kowalski, T. Yankeelov

bioRxiv, 2020

Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer

Angela M. Jarrett, Alay Shah, Meghan J. Bloom, M. McKenna, D. Hormuth, T. Yankeelov, A. Sorace

Scientific Reports, 2019

Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data.

D. Hormuth, Angela M. Jarrett, E. Lima, M. McKenna, D. Fuentes, T. Yankeelov

JCO Clinical Cancer Informatics, 2019

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

Translating preclinical MRI methods to clinical oncology

D. Hormuth, A. Sorace, J. Virostko, R. Abramson, Z. Bhujwalla, P. Enriquez-Navas, R. Gillies, J. Hazle, R. Mason, C. Quarles, J. Weis, J. Whisenant, Junzhong Xu, T. Yankeelov

Journal of Magnetic Resonance Imaging, 2019

Quantitative imaging using MRI

D. Hormuth, J. Virostko, A. Stokes, A. Dula, A. Sorace, J. Whisenant, J. Weis, C. Quarles, M. Miga, T. Yankeelov

Radiomics and Radiogenomics, 2019

Quantitative imaging to guide mechanism-based modeling of cancer

D. Hormuth, M. McKenna, T. Yankeelov

Radiomics and Radiogenomics, 2019

The 2019 mathematical oncology roadmap

R. Rockne, A. Hawkins-Daarud, K. Swanson, J. Sluka, J. Glazier, P. Macklin, D. Hormuth, Angela M. Jarrett, E. Lima, J. Tinsley Oden, G. Biros, T. Yankeelov, K. Curtius, I. Al Bakir, D. Wodarz, N. Komarova, Luis Aparicio, M. Bordyuh, R. Rabadán, S. Finley, H. Enderling, J. Caudell, E. Moros, A. Anderson, R. Gatenby, Artem Kaznatcheev, P. Jeavons, Nikhil P. Krishnan, J. Pelesko, Raoul R. Wadhwa, N. Yoon, D. Nichol, A. Marusyk, M. Hinczewski, Jacob G. Scott

Physical Biology, 2019

SCIDOT-38. DEVELOPMENT OF AN IMAGE-INFORMED MATHEMATICAL MODEL OF CONVECTION-ENHANCED DELIVERY OF NANOLIPOSOMES FOR INDIVIDUAL PATIENTS

Ryan T Woodall, D. Hormuth, M. Abdelmalik, Chengyue Wu, Xinzeng Feng, W. Phillips, A. Bao, T. Hughes, A. Brenner, T. Yankeelov

Neuro-Oncology, 2019

Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment

Ryan T Woodall, D. Hormuth, M. Abdelmalik, Chengyue Wu, Xinzeng Feng, W. Phillips, A. Bao, T. Hughes, A. Brenner, T. Yankeelov

Medical Imaging, 2019

Multi-Scale Imaging to Enable Multi-Scale Modeling for Predicting Tumor Growth and Treatment Response

T. Yankeelov, D. Hormuth, Angela M. Jarrett, E. Lima, Chengyue Wu, Ryan T Woodall, C. Philips

Biophysical Journal, 2019

Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results

Angela M. Jarrett, D. Hormuth, Stephanie L. Barnes, Xinzeng Feng, Wei Huang, T. Yankeelov

Physics in Medicine and Biology, 2018

Mathematical models of tumor cell proliferation: A review of the literature

Angela M. Jarrett, E. Lima, D. Hormuth, M. McKenna, Xinzeng Feng, David A. Ekrut, A. C. M. Resende, A. Brock, T. Yankeelov

Expert Review of Anticancer Therapy, 2018

Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors

Chengyue Wu, F. Pineda, D. Hormuth, G. Karczmar, T. Yankeelov

Magnetic Resonance in Medicine, 2018

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

Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

Hesham Rachel B. Abdallah S. R. Musaddiq J. Yao Kimberly Elhalawani Ger Mohamed Awan Ding Li Fave Beers Dri, H. Elhalawani, R. Ger, A. Mohamed, M. Awan, Yao Ding, Kimberly Li, X. Fave, Andrew Beers, B. Driscoll, David A. Hormuth II, P. V. van Houdt, R. He, Shouhao Zhou, K. Mathieu, Heng Li, C. Coolens, C. Chung, J. Bankson, Wei Huang, Jihong Wang, V. Sandulache, S. Lai, R. Howell, R. Stafford, T. Yankeelov, U. A. van der Heide, S. Frank, D. Barboriak, J. Hazle, L. Court, Jayashree Kalpathy-Cramer, C. Fuller

Scientific Data, 2018

Erratum: Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

Hesham Rachel B. Abdallah S.R. Musaddiq J. Yao Kimberly X Elhalawani Ger Mohamed Awan Ding Li Fave Beers Dri, H. Elhalawani, R. Ger, A. Mohamed, M. Awan, Yao Ding, Kimberly Li, X. Fave, Andrew Beers, B. Driscoll, David A. Hormuth II, P. V. van Houdt, R. He, Shouhao Zhou, K. Mathieu, Heng Li, C. Coolens, C. Chung, J. Bankson, Wei Huang, Jihong Wang, V. Sandulache, S. Lai, R. Howell, R. Stafford, T. Yankeelov, U. A. van der Heide, S. Frank, D. Barboriak, J. Hazle, L. Court, Jayashree Kalpathy-Cramer, C. Fuller

Scientific data, 2018

Publisher Correction: A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Rachel B. Abdallah S. R. Musaddiq J. Yao Kimberly Xenia J Ger Mohamed Awan Ding Li Fave Beers Driscoll Elhal, R. Ger, A. Mohamed, M. Awan, Yao Ding, Kimberly Li, X. Fave, Andrew Beers, B. Driscoll, H. Elhalawani, D. Hormuth, P. V. Houdt, R. He, Shouhao Zhou, K. Mathieu, Heng Li, C. Coolens, C. Chung, J. Bankson, Wei Huang, Jihong Wang, V. Sandulache, S. Lai, R. Howell, R. Stafford, T. Yankeelov, U. A. van der Heide, S. Frank, D. Barboriak, J. Hazle, L. Court, Jayashree Kalpathy-Cramer, C. Fuller

Scientific Reports, 2018

The effects of intravoxel contrast agent diffusion on the analysis of DCE‐MRI data in realistic tissue domains

Ryan T Woodall, Stephanie L. Barnes, D. Hormuth, A. Sorace, C. Quarles, T. Yankeelov

Magnetic Resonance in Medicine, 2018

An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging

Xinzeng Feng, D. Hormuth, T. Yankeelov

Computational Mechanics, 2018

ANGI-08. PREDICTING IN VIVO TUMOR GROWTH AND ANGIOGENESIS WITH AN MRI CALIBRATED BIOPHYSICAL MODEL

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

2017

Abstract A09: Predicting response to whole brain radiotherapy in a murine model of glioma

D. Hormuth, J. Weis, Stephanie B. Eldridge, M. Miga, E. Rericha, V. Quaranta, T. Yankeelov

2017

A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth

D. Hormuth, J. Weis, Stephanie L. Barnes, M. Miga, E. Rericha, V. Quaranta, T. Yankeelov

Journal of the Royal Society Interface, 2017

Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.

D. Hormuth, J. Weis, Stephanie L. Barnes, M. Miga, V. Quaranta, T. Yankeelov

International Journal of Radiation Oncology, Biology, Physics, 2017

Abstract 4530: A biophysical model for necrosis development in glioblastoma informed by subject-specific MRI measurements

D. Hormuth, T. Yankeelov

2017

Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data.

E. Lima, J. Oden, B. Wohlmuth, A. Shahmoradi, D. Hormuth, T. Yankeelov, L. Scarabosio, T. Horger

Computer Methods in Applied Mechanics and Engineering, 2017

Abstract A11: Calibration, selection and validation of tumor growth models

E. Lima, R. C. Almeida, D. Hormuth, T. Yankeelov, J. Oden

2017

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Rachel B. Abdallah S. R. Musaddiq J. Yao Kimberly Xenia J Ger Mohamed Awan Ding Li Fave Beers Driscoll Elhal, R. Ger, A. Mohamed, M. Awan, Yao Ding, Kimberly Li, X. Fave, Andrew Beers, B. Driscoll, H. Elhalawani, D. Hormuth, P. V. Houdt, R. He, Shouhao Zhou, K. Mathieu, Heng Li, C. Coolens, C. Chung, J. Bankson, Wei Huang, Jihong Wang, V. Sandulache, S. Lai, R. Howell, R. Stafford, T. Yankeelov, U. Heide, S. Frank, D. Barboriak, J. Hazle, L. Court, Jayashree Kalpathy-Cramer, C. Fuller

Scientific Reports, 2017

Selection, calibration, and validation of models of tumor growth.

E. Lima, J. Oden, D. Hormuth, T. Yankeelov, R. C. Almeida

Mathematical Models and Methods in Applied Sciences, 2016

Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

David A. Hormuth II, J. Weis, Stephanie L. Barnes, M. Miga, E. Rericha, V. Quaranta, T. Yankeelov

Physical Biology, 2015

Abstract A42: A mechanically coupled reaction-diffusion model for predicting in vivo C6 glioma growth in rats

D. Hormuth, J. Weis, M. Miga, T. Yankeelov

2015

A comparison of individual and population-derived vascular input functions for quantitative DCE-MRI in rats.

D. Hormuth, J. Skinner, M. Does, T. Yankeelov

Magnetic Resonance Imaging, 2014

MO-G-BRF-03: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - Using Quantitative MRI to Test the Validity of the Reaction-Diffusion Equation in Describing in Vivo Glioma Growth.

D. Hormuth, J. Weis, E. Rericha, T. Yankeelov

Medical physics, 2014

In vivo imaging to initialize a biophysical model of tumor growth: Preliminary results

D. Hormuth, T. Yankeelov

2013 Biomedical Sciences and Engineering Conference (BSEC), 2013

Clinically Relevant Modeling of Tumor Growth and Treatment Response

T. Yankeelov, N. Atuegwu, D. Hormuth, J. Weis, Stephanie L. Barnes, M. Miga, E. Rericha, V. Quaranta

Science Translational Medicine, 2013

DETERMINATION OF HYPOXIC TUMOR FRACTION USING MRI AND PET IN C 6 RAT BRAIN TUMORS

A. Stokes, D. Hormuth, T. Yankeelov, C. Quarles

2012

Optimization of qBOLD Methods for the Assessment of Mouse Renal Oxygenation

Feng Wang, D. Hormuth, Keiko Takahashi, J. Gore, R. Harris, Takamune Takahashi, C. Quarles

2012

Towards real-time tracking of anatomic features for HIFU beam steering

D. Hormuth, B. Zappia, A. Holbrook, K. Butts-Pauly, C. Dumoulin

2009

Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer

Anum S Kazerouni, D. Hormuth, Tessa Davis, Meghan J. Bloom, Sarah Mounho, G. Rahman, J. Virostko, T. Yankeelov, A. Sorace

Cancers, 2022