Reduced Field-of-View Intravoxel Incoherent Motion of the Human Pancreas Reflects Biphasic Response to Glucose Ingestion

Chengyue Wu, T. Yankeelov, John Virostko

ISMRM Annual Meeting

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

A. Chaudhuri, G. Pash, D. Hormuth, G. Lorenzo, Michael G. Kapteyn, Chengyue Wu, E. Lima, T. Yankeelov, K. Willcox

Frontiers in Artificial Intelligence, 2023

Abstract 845: Developing MRI-based digital-twins via mathematical modeling and deep learning to predict the response of triple-negative breast cancer to neoadjuvant therapy

Casey E. Stowers, Chengyue Wu, Sidharth Kumar, E. Lima, Xhan Zu, C. Yam, J. Son, Jingfei Ma, Jonathan I. Tamir, G. Rauch, T. Yankeelov

Cancer Research, 2023

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Chase Christenson, Chengyue Wu, D. Hormuth, Shiliang Huang, Ande Bao, Andrew J. Brenner, T. Yankeelov

Brain Multiphysics, 2023

Abstract 5569: Quantification of tumor-associated vasculature as an imaging biomarker for monitoring the response of triple-negative breast cancer to neoadjuvant chemotherapy

Chengyue Wu, Casey E. Stowers, Zhan Xu, E. Lima, C. Yam, J. Son, Jingfei Ma, G. Rauch, T. Yankeelov

Cancer Research, 2023

Optimized Patient-Specific Catheter Placement for Convection-Enhanced Nanoparticle Delivery in Recurrent Glioblastoma

Chengyue Wu, D. Hormuth, Chase Christenson, Ryan T. Woodall, M. Abdelmalik, William T. Phillips, T. J. Hughes, Andrew J. Brenner, T. Yankeelov

SC Workshops, 2023

Abstract 850: Patient-specific, organ-scale forecasting of prostate cancer growth in active surveillance

G. Gómez, Chengyue Wu, J. Yung, John F. Ward, H. Gómez, A. Reali, T. Yankeelov, A. Venkatesan, Thomas J. R. Hughes

Cancer Research, 2023

Designing clinical trials for patients who are not average

T. Yankeelov, D. Hormuth, Ernesto A.B. F. Lima, Guillermo Lorenzo, Chengyue Wu, Lois C. Okereke, Gaiane M. Rauch, A. Venkatesan, Caroline Chung

iScience, 2023

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

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

Cancer Research, 2022

Abstract 2736: Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin

Chengyue Wu, A. M. Jarrett, Zijian Zhou, N. Elshafeey, B. Adrada, R. Candelaria, Rania M M Mohamed, M. Boge, L. Huo, J. White, D. Tripathy, V. Valero, Jennifer F. Litton, C. Yam, J. Son, Jingfei Ma, G. Rauch, T. Yankeelov

Cancer Research, 2022

Abstract P1-08-08: Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer viamathematical modeling and quantitative MRI

Chengyue Wu, A. M. Jarrett, Zijian Zhou, N. Elshafeey, B. Adrada, R. Candelaria, Rania M M Mohamed, M. Boge, L. Huo, J. White, D. Tripathy, V. Valero, Jennifer F. Litton, S. Moulder, C. Yam, J. Son, Jingfei Ma, G. Rauch, T. Yankeelov

Cancer Research, 2022

MRI-based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer.

Chengyue Wu, A. M. Jarrett, Zijian Zhou, N. Elshafeey, B. Adrada, R. Candelaria, Rania M M Mohamed, M. Boge, L. Huo, J. White, D. Tripathy, V. Valero, J. Litton, C. Yam, J. Son, Jingfei Ma, G. Rauch, 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, A. 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, John Virostko, C. Phillips, D. Patt, C. Chung, T. Yankeelov

Biophysical Reviews, 2022

An untrained deep learning method for reconstructing dynamic MR images from accelerated model‐based data

Kalina P. Slavkova, J. DiCarlo, Viraj Wadhwa, Sidharth Kumar, Chengyue Wu, John Virostko, T. Yankeelov, Jonathan I. Tamir

Magnetic Resonance in Medicine, 2022

A 1D–0D–3D coupled model for simulating blood flow and transport processes in breast tissue

Marvin Fritz, T. Köppl, J. Oden, Andreas Wagner, B. Wohlmuth, Chengyue Wu

International Journal for Numerical Methods in Biomedical Engineering, 2022

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

A. M. Jarrett, Anum S. Kazerouni, Chengyue Wu, John 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

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

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 Christenson, M. Abdelmalik, W. Phillips, Thomas J. R. 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, A. M. Jarrett, G. Lorenzo, E. Lima, Chengyue Wu, C. Chung, D. Patt, T. Yankeelov

Expert Review of Precision Medicine and Drug Development, 2021

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

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

Cancers, 2021

The rate of breast fibroglandular enhancement during dynamic contrast-enhanced MRI reflects response to neoadjuvant therapy.

John Virostko, Garrett Kuketz, E. Higgins, Chengyue Wu, A. Sorace, J. DiCarlo, Sarah Avery, D. Patt, B. Goodgame, T. Yankeelov

European Journal of Radiology, 2021

Abstract PS3-26: Characterizing errors in perfusion model parameters derived from retrospectively abbreviated quantitative DCE-MRI data

Kalina P. Slavkova, J. DiCarlo, A. Syed, Chengyue Wu, John Virostko, A. Sorace, T. Yankeelov

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

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

A. M. Jarrett, D. Hormuth, Chengyue Wu, Anum S. Kazerouni, David A. Ekrut, John 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

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

2020

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

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

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

Abstract PD9-07: The rate of parenchymal enhancement during DCE-MRI reflects response to neoadjuvant therapy

John Virostko, E. Higgins, Chengyue Wu, A. Sorace, D. Patt, B. Goodgame, T. Yankeelov

2020

Abstract P6-02-04: Investigating the feasibility of performing quantitative DCE-MRI in an abbreviated breast examination

Kalina P. Slavkova, J. DiCarlo, A. Syed, Chengyue Wu, John Virostko, A. Sorace, T. Yankeelov

2020

Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge

L. Bell, N. Semmineh, H. An, C. Eldeniz, R. Wahl, K. Schmainda, M. Prah, B. Erickson, P. Korfiatis, Chengyue Wu, A. Sorace, T. Yankeelov, N. Rutledge, T. Chenevert, D. Malyarenko, Yichu Liu, A. Brenner, Leland S. Hu, Yuxiang Zhou, J. Boxerman, Yi-Fen Yen, Jayashree Kalpathy-Cramer, Andrew L Beers, M. Muzi, A. Madhuranthakam, M. Pinho, Brian Johnson, C. Quarles

Tomography, 2020

Abstract P1-01-02: Quantitative breast MRI to predict response to neoadjuvant therapy in community imaging centers: Preliminary results

A. Sorace, John Virostko, Chengyue Wu, A. M. Jarrett, Stephanie L. Barnes, David A. Ekrut, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

Poster Session Abstracts, 2019

Magnetization Transfer MRI of Breast Cancer in the Community Setting: Reproducibility and Preliminary Results in Neoadjuvant Therapy

John Virostko, A. Sorace, Chengyue Wu, David A. Ekrut, A. M. Jarrett, Raghave M. Upadhyaya, Sarah Avery, D. Patt, B. Goodgame, T. Yankeelov

Tomography, 2019

Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO)

L. Bell, N. Semmineh, H. An, C. Eldeniz, R. Wahl, K. Schmainda, M. Prah, B. Erickson, P. Korfiatis, Chengyue Wu, A. Sorace, T. Yankeelov, N. Rutledge, T. Chenevert, D. Malyarenko, Yichu Liu, A. Brenner, Leland S. Hu, Yuxiang Zhou, J. Boxerman, Yi-Fen Yen, Jayashree Kalpathy-Cramer, Andrew L Beers, M. Muzi, A. Madhuranthakam, M. Pinho, Brian Johnson, C. Quarles

Tomography, 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

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

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

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

Biophysical Journal, 2019

Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting

A. Sorace, Chengyue Wu, Stephanie L. Barnes, A. M. Jarrett, Sarah Avery, D. Patt, B. Goodgame, Jeffery J Luci, Hakmook Kang, R. Abramson, T. Yankeelov, John Virostko

Journal of Magnetic Resonance Imaging, 2018

Abstract 3043: Quantitative MRI during neoadjuvant therapy for predicting breast cancer response in the community setting

A. Sorace, John Virostko, Chengyue Wu, A. M. Jarrett, Stephanie L. Barnes, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

Tumor Biology, 2018

Abstract P4-02-08: Repeatability and reproducibility of quantitative breast MRI in community imaging centers: Preliminary results

A. Sorace, John Virostko, Chengyue Wu, A. M. Jarrett, Stephanie L. Barnes, J. Luci, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov

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