Chengyue Wu
Assistant Professor
Contact
I am a researcher in computational oncology.
My research interests mostly focus on integrating emerging biomedical imaging techniques with computational and mathematical modeling, so to improve the diagnosis, prognosis, and treatment of human cancers.
I am an Assistant Professor at the Department of Imaging Physics in The University of Texas MD Anderson Cancer Center, with joint appointments with the Department of Breast Imaging, Department of Biostatistics, and Institute for Data Science in Oncology. I am also an affiliated member of the Center for Computational Oncology at the Oden Institute in The University of Texas at Austin.
I have extensive experience on developing and validating image processing methods and image-guided models for investigating tumor growth and treatment response, tumor-associated vasculature and microenvironment, and drug delivery. My current projects focus on
1) Image-guided computational modeling (“digital twins”) to predict and optimize cancer (especially breast cancer) treatment response on a patient-specific basis.
2) Development of deep learning models, longitudinal image analysis, and multi-modality data integration to improve breast cancer early detection.
Check out my research below and feel free to contact me if you are interested in discussing my work or any of the topics in this webpage.
My research interests mostly focus on integrating emerging biomedical imaging techniques with computational and mathematical modeling, so to improve the diagnosis, prognosis, and treatment of human cancers.
I am an Assistant Professor at the Department of Imaging Physics in The University of Texas MD Anderson Cancer Center, with joint appointments with the Department of Breast Imaging, Department of Biostatistics, and Institute for Data Science in Oncology. I am also an affiliated member of the Center for Computational Oncology at the Oden Institute in The University of Texas at Austin.
I have extensive experience on developing and validating image processing methods and image-guided models for investigating tumor growth and treatment response, tumor-associated vasculature and microenvironment, and drug delivery. My current projects focus on
1) Image-guided computational modeling (“digital twins”) to predict and optimize cancer (especially breast cancer) treatment response on a patient-specific basis.
2) Development of deep learning models, longitudinal image analysis, and multi-modality data integration to improve breast cancer early detection.
Check out my research below and feel free to contact me if you are interested in discussing my work or any of the topics in this webpage.
Projects
Automatic longitudinal mammography analysis for large breast cancer screening cohort
A processing pipeline for longitudinal mammography registration, interpretation, and automatic image labeling using text-based radiology reports to enable efficient and comprehensive imaging data analysis.
Digital twins to patient-specifically optimize breast cancer response to chemotherapy
Image-guided digital twins provide the unique opportunity to systematically evaluate individual breast cancer patient’s response to different chemotherapy dosing-schedule, thereby optimizing the treatment plan.
Patient-specific optimization of nanoparticle convection-enhanced delivery in rGBM
Image-guided model achieved high accuracy for predicting radioactive nanoparticle delivery for rGBM and guided patient-specific placement of delivery catheter(s), so to improving treatment efficacy and reducing side effects.
Image-guided digital twins to predict breast cancer response to neoadjuvant therapy
Integrating MRI data with mechanism-based mathematical modeling successfully predicts breast cancer response to neoadjuvant therapy on a patient-specific basis
Quantitative MRI to characterize tumor microenvironment, vasculature, and blood supply
Novel approaches integrating quantitative MRI analysis and computational fluid dynamics to identify tumor-associated vasculature and to estimate the blood supply and interstitial fluid environment for breast tumors.
in silico MRI validation framework
We developed an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods.
