Chengyue Wu, PhD
Department of Imaging Physics, Division of Diagnostic Imaging
About Dr. Chengyue Wu
Dr. Chengyue Wu is an Assistant Professor in the Department of Imaging Physics, University of Texas MD Anderson Cancer Center, with joint appointments in the Department of Biostatistics and Breast Imaging, and affiliation with Institute for Data Science in Oncology. Dr. Wu’s research interests focus on computational oncology, especially integrating emerging biomedical imaging techniques with computational modeling, to improve the diagnosis, prognosis, and treatment of human cancers. She has authored or co-authored over 25 journal articles and over 60 conference abstracts or papers, and has been regularly serving as a co-organizer, executive committee member, and moderator/reviewer for multiple scientific conferences. She was awarded the prestigious H. D. Landahl Mathematical Biophysics Award in 2023 from the Society of Mathematical Biology for her contributions to both diagnosing breast cancers and predicting their response to therapy, as well as the ready generalizability of her techniques to other solid tumor types.
Present Title & Affiliation
Primary Appointment
Assistant Professor, Imaging Physics - Research, The University of Texas MD Anderson Cancer Center, Houston, TX
Assistant Professor (Joint appointment), Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
Assistant Professor (Joint appointment), Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
Education & Training
Degree-Granting Education
| 2020 | The University of Texas at Austin, Austin, Texas, US, Biomedical Engineering, Ph.D |
| 2016 | University of Science & Technology of China (USTC), Heifei, CN, Biosciences, BS |
Postgraduate Training
| 2020-2023 | Postdoctoral Fellow, Oden Institute for Computational Engineering and Sciences, Austin, Texas |
Experience & Service
Other Professional Positions
IGCT Investigator, The University of Texas MD Anderson Cancer Center, Houston, Texas, 2025 - Present
Regular Member, The University of Texas MD Anderson Cancer Center, Houston, TX, 2024 - Present
Affiliate, The University of Texas MD Anderson Cancer Center, Houston, Tx, 2023 - Present
Affiliate, The University of Texas at Austin, Austin, TX, 2023 - Present
Graduate Research Assistant, The University of Texas at Austin, Austin, TX, 2016 - 2020
Extramural Institutional Committee Activities
Member, QS Program Admissions Subcommittee, UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, 2025 - Present
Program Committee Member, Image Data Science Interest Group, World Molecular Imaging Society, 2025 - Present
Executive Committee Member, Mathematical Oncology Subgroup, Society for Mathematical Biology, 2020 - Present
Editorial Activities
Guest Editor, Mathematical Biosciences and Engineering, 2023 - 2024
Guest Editor, Cancers, 2022 - 2025
Guest Editor, Tomography, 2021 - 2023
Honors & Awards
| 2026 - Present | The Division of Diagnostic Imaging Junior Faculty Award, The University of Texas MD Anderson Cancer Center |
| 2026 - Present | Invited Participant & Travel Grant for ICERM Workshop on "Tensor Analysis for Large-Scale Data", National Science Foundation |
| 2025 - Present | USNCCM18 Travel Award, U.S. National Congress on Computational Mechanics |
| 2024 | Nominated Member, Academy’s Council of Early Career Investigators in Imaging (CECI²) Class of 2024 - 2025 |
| 2023 | H. D. Landahl Mathematical Biophysics Award, Society of Mathematical Biology |
| 2023 | Travel Grants for the Association for Women in Mathematics 2023 Research Symposium, AWM |
| 2023 | NSF Sponsored Scholarship for attending The Academic Life Workshop, 17th U.S. National Congress on Computational Mechanics |
| 2023 | USNCCM17 Best Presentation Award (second place in postdoc category), Female Researchers Chapter (FRC) of the International Association for Computational Mechanics (IACM) |
| 2022 | Travel Award for NIDDK Pancreatic Disease Workshop Short Talk Speaker, NIDDK |
| 2022 | Trainee Stipend for ISMRM Workshop on Cancer Imaging, ISMRM |
| 2022 | Trainee Stipend for Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, ISMRM |
| 2022 | Travel Grants for the Association for Women in Mathematics 2022 Research Symposium, AWM |
| 2021 | Finalist, ISMRM Junior Fellow Symposium: Shark Tank, ISMRM |
| 2020 | Magna Cum Laude 2020 ISMRM Merit Award, ISMRM |
| 2020 | Graduate Student Professional Development Award, The University of Texas at Austin |
| 2020 | Selected Attendee of Rising Stars 2020 Workshop, Rising Star |
| 2020 | Trainee Stipend for ISMRM 28th Annual Meeting, ISMRM |
| 2019 | Trainee Stipend for ISMRM 27th Annual Meeting, ISMRM |
| 2016 - 2020 | Cockrell School of Engineering fellowship, The University of Texas at Austin |
| 2016 | Excellent Graduation Thesis (Top 5%), USTC |
| 2016 | Honorary Rank (Top 3%), USTC |
| 2015 | J. Z. Chen Scholarship for Research Potential, ZLR Valeon Scholar Program |
| 2015 | Yang Ya Foundation Scholarship (Top 5%), Yang Ya Foundation |
| 2014 | Outstanding Student Scholarship, silver medalist (Top 5%), USTC |
| 2013 | National Scholarship (Top 3%), Ministry of Education of China |
| 2012 - 2015 | Grants for Training Plan of National Basic Subject Top-Notch Talent, Ministry of Education of China |
| 2012 | Excellent Freshman Award, USTC Initiative Foundation |
Professional Memberships
Selected Presentations & Talks
Local Presentations
- 2025. Foundational advancement toward practical integration of cancer digital twins into a clinical workflow. Invited. 2025 JCCO Annual Retreat. Houston, Texas, US.
- 2025. Patient-Derived Models to Enhance Digital Twin Prediction of Experimental Therapeutics. Invited. 2nd Annual Digital Twin Summit: Computational Modeling in Medical Oncology. Houston, Texas, US.
- 2025. Image-guided cancer patient digital twins for personalized healthcare. Invited. Institute for Data Science in Oncology Focus Area Forum. Houston, Texas, US.
National Presentations
- 2026. Multi-scale computational modeling of tumor vasculature: integrating anatomical and functional DCE-MRI data for sarcoma treatment monitoring. Invited. 2026 SIAM Conference on the Life Sciences. Cleveland, Ohio, US.
- 2026. Toward Development and Deployment of MRI-guided Digital Twins for Neoadjuvant Therapy of Triple Negative Breast Cancer. Invited. 2026 SIAM Conference on the Life Sciences. Cleveland, Ohio, US.
- 2026. Multi-regional CT-based radiomics fusion predicts pathological pollutant index associated with lung adenocarcinoma. Conference. American Association for Cancer Research Annual Meeting 2026. San Diego, California, US.
- 2025. MRI-informed personalized predictions of tumor progression to inform active surveillance of prostate cancer. Invited. 18th U.S. National Congress on Computational Mechanics (USNCCM18). Chicago, IL, US.
- 2025. MRI-informed mathematical model to practically guide patient-specific optimization of triple-negative breast cancer response to neoadjuvant chemotherapy. Invited. 18th U.S. National Congress on Computational Mechanics (USNCCM18). Chicago, IL, US.
- 2025. Predicting and optimizing the response of breast cancer patients to neoadjuvant therapy using a multiscale computational model. Conference. AACR Annual Meeting 2025. Chicago, Illinois, US.
- 2025. MRI-informed mathematical model to guide patient-specific optimization triple negative breast cancer response to neoadjuvant chemotherapy. Invited. 2025 Joint Mathematics Meetings (JMM 2025). Seattle, Washington, US.
- 2024. Retrospective validation of digital twin-based predictions of personalized triple negative breast cancer response to neoadjuvant therapy regimens. Conference. San Antonio Breast Cancer Symposium 2024. San Antonio, Texas, US.
- 2024. Predicting the response of locally advanced breast cancer to neoadjuvant therapy using MRI-based mathematical modeling of the I-SPY 2 dataset. Conference. San Antonio Breast Cancer Symposium 2024. San Antonio, Texas, US.
- 2024. Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis. Conference. San Antonio Breast Cancer Symposium 2024. San Antonio, Texas, US.
- 2024. Using a Mechanism-Based Mathematical Model and Multiparametric MRI to Predict Response to Therapy of I-SPY 2 Breast Cancer Patients. Invited. 10th Computational Approaches for Cancer Workshop. Atlanta, Georgia, US.
- 2024. Using MRI-based mathematical modeling to predict the response of breast cancer to neoadjuvant therapy. Conference. BMES Annual Meeting 2024. Baltimore, Maryland, US.
- 2024. Image-guided mathematical modeling for patient- specific prediction and optimization of breast cancer response to neoadjuvant therapy. Invited. SIAM Conference in Life Science 2024. Portland, Oregon, US.
- 2024. Using pre-treatment MRI data to drive an integrated mechanism- and data-driven model to predict the response of breast cancer to therapy. Invited. SIAM Conference in Imaging Science 2024. Atlanta, Georgia, US.
- 2024. Computational modeling of magnetic nanoparticle hyperthermia to predict the distribution of intratumoral temperatures: Preliminary study. Conference. Society for Thermal Medicine Annual Meeting 2024. Houston, Texas, US.
- 2024. Personalized MRI-informed forecasting of prostate cancer progression during active surveillance. Conference. 2024 American Association for Cancer Research Annual Meeting. San Diego, California, US.
- 2023. Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to therapy. Conference. San Antonio Breast Cancer Symposium 2023. San Antonio, Texas, US.
- 2023. Optimizing therapeutic regimens via digital twins to improve triple negative breast cancer response to neoadjuvant therapy. Conference. San Antonio Breast Cancer Symposium 2023. San Antonio, Texas, US.
- 2023. Optimized Patient-Specific Catheter Placement for Convection-Enhanced Nanoparticle Delivery in Recurrent Glioblastoma. Invited. NCI 9th Computational Approaches for Cancer Workshop. Denver, Colorado, US.
- 2023. Introducing mechanical stress into a reduced order model for breast cancer response to chemotherapy. Invited. Biomedical Engineering Society annual meeting 2023. Seattle, Washington, US.
- 2023. Patient-specific, imaging-informed computational forecasting of prostate cancer growth during active surveillance. Invited. 17th US National Conference on Computational Mechanics. Albuquerque, New Mexico, US.
- 2023. Patient-Specific predictive digital twin for optimizing radiotherapy regimens under uncertainty in high-grade gliomas. Invited. 17th US National Conference on Computational Mechanics. Albuquerque, New Mexico, US.
- 2023. Towards a practical framework for n-1 clinical trials. Invited. 17th U.S. National Congress on Computational Mechanics. Albuquerque, NM, US.
- 2023. Developing MRI-based digital-twins via mathematical modeling and deep learning to predict the response of triple-negative breast cancer to neoadjuvant therapy. Conference. AACR Annual Meeting 2023. Orlando, Florida, US.
- 2023. Quantification of tumor-associated vasculature as an imaging biomarker for monitoring the response of triple-negative breast cancer to neoadjuvant chemotherapy. Conference. AACR Annual Meeting 2023. Orlando, Florida, US.
- 2023. Patient-specific, organ-scale forecasting of prostate cancer growth during active surveillance. Conference. AACR Annual Meeting 2023. Orlando, Florida, US.
- 2022. Forecasting patient-specific treatment response to neoadjuvant systemic therapy in triple negative breast cancer via mathematical modeling and quantitative magnetic resonance imaging. Invited. 2022 AWM Research Symposium. Minneapolis, MN, US.
- 2022. A biology-based, mathematical model to predict the response of recurrent glioblastoma to treatment with 186Re-labeled nanoliposomes. Conference. AACR Annual Meeting 2022. New Orleans, Louisiana, US.
- 2022. Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin. Conference. AACR Annual Meeting 2022. New Orleans, Louisiana, US.
- 2021. Forecasting patient specific treatment response to neoadjuvant systemic therapy in triple negative breast cancer via mathematical modeling and quantitative magnetic resonance imaging. Conference. San Antonio Breast Cancer Symposium 2021. San Antonio, Texas, US.
- 2021. Towards image-guided modeling of patient-specific rhenium-186 nanoliposome distribution via convection-enhanced delivery for glioblastoma multiforme. Conference. SNO Annual Meeting 2021. Boston, Massachusetts, US.
- 2021. Optimized patient-specific catheter placement for convection-enhanced nanoparticle delivery in GBM. Invited. BMES Annual Meeting 2021. Orlando, Florida, US.
- 2021. Image-informed Mathematical Modeling to Predict Patient- Specific Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer. Invited. 7th Computational Approaches for Cancer Workshop. St. Louis, Missouri, US.
- 2021. Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-based fluid dynamics. Conference. AACR Annual Meeting 2021. Virtual.
International Presentations
- 2026. Foundational advancement toward development and deployment of digital twins for neoadjuvant therapy of triple negative breast cancer. Invited. 17th World Congress on Computational Mechanics (WCCM). Munich, DE.
- 2026. Dual-scale vascular analysis of undifferentiated pleomorphic sarcoma using DCE-MRI for spatially resolved treatment response assessment. Conference. European Congress of Radiology 2026. Vienna, AT.
- 2026. Development of an Automated Mammography-Based Skin Segmentation Approach for the Monitoring of Inflammatory Breast Cancer. Conference. IEEE 23rd International Symposium on Biomedical Imaging (ISBI). London, GB.
- 2025. Model-based response prediction and personalization of neoadjuvant chemotherapy in triple-negative breast cancer. Conference. ISMRM Workshop on Breast MRI. Las Vegas, US.
- 2025. MRI- based mathematical modeling to predict the response of cervical cancer patients to chemoradiation. Invited. SMB Annual Meeting 2025. Edmonton, CA.
- 2025. MRI-informed mechanistic model to guide patient-specific optimization triple-negative breast cancer response to neoadjuvant chemotherapy. Invited. XI International Conference on Coupled Problems in Science and Engineering. Villasimius, IT.
- 2025. Applying an MRI-based mathematical model to predict the response of cervical cancer to chemoradiation: Preliminary results. Conference. ISMRM Annual Meeting 2025. Honolulu, US.
- 2025. Intravoxel Incoherent Motion (IVIM) MRI of the Human Pancreas Detects Altered Glucose-Stimulated Pancreas Perfusion in Type 1 Diabetes. Conference. ISMRM Annual Meeting 2025. Honolulu, US.
- 2025. Predicting Cervical Cancer Response to Chemoradiation Using MRI-Based Mathematical Modeling. Conference. IEEE 22nd International Symposium on Biomedical Imaging (ISBI). Houston, US.
- 2025. Spatio-Temporal Attention Fusion Model for Breast Cancer Detection. Conference. IEEE 22nd International Symposium on Biomedical Imaging (ISBI). Houston, US.
- 2024. Patient-specific optimization of therapeutic regimens via digital twins to improve triple negative breast cancer response to neoadjuvant therapy. Invited. The 16th World Congress on Computational Mechanics (WCCM 2024). Vancouver, CA.
- 2024. Integrating mechanism-based modeling and MRI data to improve predications of breast cancer response to chemotherapy. Invited. SMB Annual Meeting 2024. Seoul, KR.
- 2024. Personalized imaging-informed forecasting of prostate cancer progression during active surveillance. Invited. 9th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2024). Lisbon, PT.
- 2024. Predicting the response of I-SPY 2 breast cancer patients to treatment using a biology-based mathematical model calibrated with quantitative MRI. Conference. 2024 ISMRM & ISMRT Annual Meeting & Exhibition. Singapore, SG.
- 2023. Integrating quantitative MRI with computational modeling to predict the response of breast cancers to neoadjuvant therapy. Invited. 10th International Congress on Industrial and Applied Mathematics. Tokyo, JP.
- 2023. Personalized MRI-informed predictions of prostate cancer growth during active surveillance. Invited. SMB Annual Meeting 2023. Columbus, US.
- 2023. Optimization of a longitudinal imaging protocol to monitor the response of breast cancer to neoadjuvant therapy via Bayesian-based data assimilation. Invited. SMB Annual Meeting 2023. Columbus, US.
- 2022. MRI-based digital twins forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer. Invited. ISMRM Workshop on Cancer Imaging: From Discovery to Diagnosis. Pacific Grove, US.
- 2022. Reduced Field-of-View Intravoxel Incoherent Motion of the Human Pancreas Reflects BiphasicResponse to Glucose Ingestion. Conference. Joint Annual Meeting ISMRM-ESMRMB. Berkeley, US.
- 2021. Towards patient-specific prediction of breast cancer response to neoadjuvant therapy. Invited. SMB Annual Meeting 2021. Virtual.
- 2021. Kinetic parameters derived from ultrafast DCE-MRI to predict breast cancer response to neoadjuvant chemotherapy. Conference. 2021 ISMRM & SMRT Annual Meeting & Exhibition - An Online Experience. Virtual.
- 2021. Predicting therapeutic response via quantitative MRI. Conference. 2021 ISMRM & SMRT Annual Meeting & Exhibition - An Online Experience. Virtual.
Formal Peers
- 2026. Image-Guided Breast Cancer Digital Twins for Personalized Healthcare. Houston, Texas, US.
- 2025. Towards practical digital twins to predict and optimize TNBC treatment response. Columbia, South Carolina, US.
- 2025. Predicting response to chemoradiation for cervical cancer patients with an MRI- based mathematical model. Conference. Tampa, Florida, US.
- 2025. Image-guided cancer patient digital twins for personalized healthcare. Invited. Tucson, Arizona, US.
- 2023. Forecasting patient-specific treatment response to neoadjuvant systemic therapy in triple negative breast cancer via MRI-based digital twins. Invited.
- 2023. Personalized forecasting of prostate cancer growth during active surveillance using an imaging- informed biomechanistic model. Invited. Austin, Texas, US.
- 2023. MRI-based digital twins to forecast treatment response in breast cancer patients. Bethesda, Maryland, US.
- 2023. MRI-based digital twins for the patient-specific prediction and optimization of neoadjuvant chemotherapy response in triple-negative breast cancer. Petrópolis, Rio de Janeiro, BR.
- 2022. Forecasting patient-specific treatment response to neoadjuvant chemotherapy in triple-negative breast cancer via MRI-based digital twins. Invited. Bethesda, Maryland, US.
- 2022. MRI Reflects Increases in Pancreas Perfusion Following Glucose Ingestion. Invited. Bethesda, Maryland, US.
- 2021. Image-informed Mathematical Modeling to Predict Patient-Specific Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer. Invited. Austin, TX, US.
- 2021. Image-guided drug delivery and tumor growth model for predicting breast cancer response to neoadjuvant therapy. Invited. Bethesda, Maryland, US.
- 2021. Optimization of patient-specific neoadjuvant therapy regimens for breast cancer via image-guided fluid dynamics modeling. Invited, US.
- 2020. Patient-specific optimization of drug injection protocols for breast cancer based on image-based fluid dynamics simulation. Invited.
- 2017. Automatic Tumor Associated Vasculature Detection and Dynamic Modeling: Preliminary Results. Invited. Nashville, Tennessee, US.
Selected Publications
Peer-Reviewed Articles
- Nandyala, M, Lanham, SA, Jha, PK, Wu, C, Hazle, JD, Yankeelov, TE, Stafford, RJ, El-Gendy, AA, Fuentes, DA. An information-theoretic framework for optimal experimental design in magnetic nanoparticle hyperthermia. Applied Mathematical Modelling 157, 2026. e-Pub 2026.
- Nandyala M, Lanham SA, Jha PK, Wu C, Hazle JD, Yankeelov TE, Stafford RJ, El-Gendy AA, Fuentes D. An information-theoretic framework for optimal experimental design in magnetic nanoparticle hyperthermia. Appl Math Model 157:116861, 2026. e-Pub 2026.
- Christenson C, Wu C, Hormuth DA, Ma J, Yam C, Rauch GM, Yankeelov TE. Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin. npj Systems Biology and Applications 11(1), 2025. e-Pub 2025. PMID: 40410237.
- Valenzuela Perez RF, Duran Sierra EJ, Antony M, Amini B, Lo S, Torres KE, Benjamin RS, Ma J, Hwang K, Stafford RJ, Araujo DM, Bishop AJ, Ratan R, Wang W, Espinoza J, Valenzuela PV, Wu C, Madewell JE, Murphy Jr WA, Costelloe C. Building a pre-surgical multiparametric-MRI-based morphologic, qualitative, semiquantitative, first and high-order radiomic predictive treatment response model for undifferentiated pleomorphic sarcoma to replace RECIST. Cancer Imaging 25(1), 2025. e-Pub 2025. PMID: 40281609.
- Stowers C, Wu C, Yam C, Ma J, Rauch GM, Yankeelov TE. Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis. Sci Rep 15(1), 2025. e-Pub 2025. PMID: 41298623.
- Patel RJS, Wu C, Stowers CE, Mohamed RM, Ma J, Rauch GM, Yankeelov TE. MRI-Based Mathematical Modeling to Predict the Response of I-SPY2 Patients with Breast Cancer to Neoadjuvant Therapy. Clin Cancer Res 31(22):4846-4856, 2025. e-Pub 2025. PMID: 40857110.
- Valenzuela RF, Duran-Sierra E, Antony M, Lo SLH, Amini B, Torres KE, Araujo D, Benjamin RS, Ma J, Hwang KP, Stafford RJ, Wu C, Madewell JE, Espinoza JV, Murphy WA, Cueto A, Valenzuela PV, Miranda-Zarate C, Costelloe CM. Pilot Study on Evaluating Multiparametric MRI (mp-MRI) Including Contrast-Enhanced Susceptibility Imaging (CE-SWI) in Predicting Pathology Treatment Response in Rhabdomyosarcoma. JRO, 2025. e-Pub 2025.
- Wu C, Lima EABF, Stowers CE, Xu Z, Yam C, Son JB, Ma J, Rauch GM, Yankeelov TE. MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens. NPJ Digit Med 8(1):195, 2025. e-Pub 2025. PMID: 40195521.
- Stowers CE, Wu C, Xu Z, Kumar S, Yam C, Son JB, Ma J, Tamir JI, Rauch GM, Yankeelov TE. Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. Radiology: Artificial Intelligence 7(1), 2025. e-Pub 2025. PMID: 39503605.
- Xie T, Gong J, Zhao Q, Wu C, Wu S, Peng W, Gu Y. Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer. BMC medical imaging 24(1), 2024. e-Pub 2024. PMID: 38844842.
- Christenson C, Wu C, Hormuth DA, Stowers CE, LaMonica M, Ma J, Rauch GM, Yankeelov TE. Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition. Journal of Computational Science 82, 2024. e-Pub 2024. PMID: 40303598.
- Wu C, Hormuth DA, Christenson C, Woodall RT, Abdelmalik MR, Phillips WT, Hughes TJ, Brenner AJ, Yankeelov TE. Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in recurrent glioblastoma. Computers in Biology and Medicine 179, 2024. e-Pub 2024. PMID: 39032243.
- Valenzuela RF, Duran-Sierra E, Canjirathinkal M, Amini B, Torres KE, Benjamin RS, Ma J, Wang WL, Hwang KP, Stafford RJ, Wu C, Zarzour AM, Bishop AJ, Lo S, Madewell JE, Kumar R, Murphy WA, Costelloe CM. Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response. Sci Rep, 2024. e-Pub 2024. PMID: 39289469.
- Wu C, Hormuth DA 2nd, Easley T, Pineda F, Karczmar GS, Yankeelov TE. Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom. J Med Imaging (Bellingham) 11(2):024002, 2024. e-Pub 2024. PMID: 38463607.
- Christenson C, Wu C, Hormuth DA, Huang S, Bao A, Brenner A, Yankeelov TE. Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes. Brain Multiphys 5, 2023. e-Pub 2023. PMID: 38187909.
- Chaudhuri A, Pash G, Hormuth DA, Lorenzo G, Kapteyn M, Wu C, EABF L, Yankeelov TE, Willcox K. Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. Front Artif Intell 6:1222612, 2023. e-Pub 2023. PMID: 37886348.
- Phillips C, Lima EA, Wu C, Jarrett AM, Zhou Z, Elshafeey N, Ma J, Rauch GM, Yankeelov TE. Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting. Journal of Computational Science 69, 2023. e-Pub 2023. PMID: 40777981.
- Slavkova KP, DiCarlo JC, Wadhwa V, Kumar S, Wu C, Virostko J, Yankeelov TE, Tamir JI. An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data. Magn Reson Med 89(4):1617-1633, 2023. e-Pub 2023. PMID: 36468624.
- Wu C, Hormuth DA, Lorenzo G, Jarrett AM, Pineda F, Howard FM, Karczmar GS, Yankeelov TE. Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics. IEEE Trans Biomed Eng 69(11):3334-3344, 2022. e-Pub 2022. PMID: 35439121.
- Wu C, Jarrett AM, Zhou Z, Elshafeey N, Adrada BE, Candelaria RP, Mohamed RMM, Boge M, Huo L, White JB, Tripathy D, Valero V, Litton JK, Yam C, Son JB, Ma J, Rauch GM, Yankeelov TE. MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Cancer Res 82(18):3394-3404, 2022. e-Pub 2022. PMID: 35914239.
- Fritz M, Köppl T, Oden JT, Wagner A, Wohlmuth B, Wu C. A 1D-0D-3D coupled model for simulating blood flow and transport processes in breast tissue. Int J Numer Method Biomed Eng 38(7):e3612, 2022. e-Pub 2022. PMID: 35522186.
- Wu C, Hormuth DA, Easley T, Eijkhout V, Pineda F, Karczmar GS, Yankeelov TE. An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom. Med Image Anal 73:102186, 2021. e-Pub 2021. PMID: 34329903.
- Woodall RT, Hormuth II DA, Wu C, Abdelmalik MRA, Phillips WT, Bao A, Hughes TJR, Brenner AJ, Yankeelov TE. Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme. Biomed Phys Eng Express 7(4), 2021. e-Pub 2021. PMID: 34050041.
- Virostko J, Kuketz G, Higgins E, Wu C, Sorace AG, DiCarlo JC, Avery S, Patt D, Goodgame B, Yankeelov TE. The rate of breast fibroglandular enhancement during dynamic contrast-enhanced MRI reflects response to neoadjuvant therapy. Eur J Radiol 136:109534, 2021. e-Pub 2021. PMID: 33454460.
- Hormuth DA, Jarrett AM, Lorenzo G, EABF L, Wu C, Chung C, Patt D, Yankeelov TE. Math, magnets, and medicine: enabling personalized oncology. Expert Rev Precis Med Drug Dev 6(2):79-81, 2021. e-Pub 2021. PMID: 34027102.
- Jarrett AM, Hormuth DA, Wu C, Kazerouni AS, Ekrut DA, Virostko J, Sorace AG, DiCarlo JC, Kowalski J, Patt D, Goodgame B, Avery S, Yankeelov TE. Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data. Neoplasia 22(12):820-830, 2020. e-Pub 2020. PMID: 33197744.
- Wu C, Hormuth DA, Oliver TA, Pineda F, Lorenzo G, Karczmar GS, Moser RD, Yankeelov TE. Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE Trans Med Imaging 39(9):2760-2771, 2020. e-Pub 2020. PMID: 32086203.
- Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC. 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. Tomography 6(2):203-208, 2020. e-Pub 2020. PMID: 32548297.
- Wu C, Pineda F, Hormuth DA, Karczmar GS, Yankeelov TE. Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors. Magn Reson Med 81(3):2147-2160, 2019. e-Pub 2019. PMID: 30368906.
- Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC. 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). Tomography 5(1):110-117, 2019. e-Pub 2019. PMID: 30854448.
- Virostko J, Sorace AG, Wu C, Ekrut D, Jarrett AM, Upadhyaya RM, Avery S, Patt D, Goodgame B, Yankeelov TE. Magnetization Transfer MRI of Breast Cancer in the Community Setting: Reproducibility and Preliminary Results in Neoadjuvant Therapy. Tomography 5(1):44-52, 2019. e-Pub 2019. PMID: 30854441.
- Yankeelov T, Hormuth D, Jarrett A, Ernesto L, Wu C, Woodall R, Philips C. Multi-Scale Imaging to Enable Multi-Scale Modeling for Predicting Tumor Growth and Treatment Response. Biophysical Journal 116(3):323a-324a, 2019. e-Pub 2019.
- Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J. Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. J Magn Reson Imaging, 2018. e-Pub 2018. PMID: 29570895.
Review Articles
- Patel RJS, Das A, Anil A, Patel G, Woodall RT, Christenson C, Thiagarajan T, Lima EABF, Lorenzo G, Hormuth II DA, Rockne RC, Adhikarla V, Wu C, Yankeelov TE. Integrating imaging and mathematical modeling to predict and optimize patient outcomes in oncology. npj Syst Biol Appl, 2026. e-Pub 2026. PMID: 42271155.
- Wu, C, Andaloussi, MA, Hormuth, DA, Lima, EA, Lorenzo, G, Stowers, CE, Ravula, S, Levac, B, Dimakis, AG, Tamir, JI, Brock, K, Chung, C, Yankeelov, TE. A critical assessment of artificial intelligence in magnetic resonance imaging of cancer. NPJ Imaging 3(1), 2025. e-Pub 2025. PMID: 40226507.
- Wu C, Andaloussi MA, Hormuth DA 2nd, Lima EABF, Lorenzo G, Stowers CE, Ravula S, Levac B, Dimakis AG, Tamir JI, Brock KK, Chung C, Yankeelov TE. A critical assessment of artificial intelligence in magnetic resonance imaging of cancer. Npj Imaging 3(1):15, 2025. e-Pub 2025. PMID: 40226507.
- Yankeelov TE, Hormuth DA, EABF L, Lorenzo G, Wu C, Okereke LC, Rauch GM, Venkatesan AM, Chung C. Designing clinical trials for patients who are not average. iScience 27(1):108589, 2024. e-Pub 2024. PMID: 38169893.
- Wu C, Lorenzo G, Hormuth DA, EABF L, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. Biophys Rev (Melville) 3(2):021304, 2022. e-Pub 2022. PMID: 35602761.
- Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 16(11):5309-5338, 2021. e-Pub 2021. PMID: 34552262.
- Hormuth DA, Phillips CM, Wu C, EABF L, Lorenzo G, Jha PK, Jarrett AM, Oden JT, Yankeelov TE. Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers (Basel) 13(12), 2021. e-Pub 2021. PMID: 34208448.
Book Chapters
- Lorenzo G, Hormuth DA 2nd, Wu C, Pash G, Chaudhuri A, Lima EA, Okereke LC, Patel R, Willcox K, Yankeelov TE. Validating the predictions of mathematical models describing tumor growth and treatment response.
Patents
- Yankeelov TE, Karcsmar G, Wu C, Hormuth DA, Oliver TA, Moser RD, Pineda F, Easley T, Barber RF, Kim B, Sheth D, Oto A, Abe H, Medved M, Fan X, Chatterjee A, Wang S. Characterization of Lesions via Determination of Vascular Metrics Using MRI Data. Patent Number: WO2021067853.
- Yankeelov TE, Jarrett AM, Hormuth DA, Kazerouni AS, Wu C, Barnes S. Quantitative Magnetic Resonance Imaging and Tumor Forecasting. Patent Number: WO/2023/049207.
Patient Reviews
CV information above last modified September 03, 2026