Chengyue Wu

Biography

Dr. Chengyue Wu is a Postdoctoral Research Fellow at the Oden Institute. She completed her Ph.D. in Biomedical Engineering at The University of Texas at Austin in the summer of 2020.

Her research interest is focused on the interdisciplinary field of computational medicine and biomedical imaging. Here at the Oden Institute, her research is focused on the imaging-based mathematical oncology. Specifically, she has been extending her previous work on image-guided fluid dynamic model to predict drug distribution throughout the breast, which enables patient-specific optimization of the neoadjuvant treatment protocol for breast cancer. She is also working as a primary investigator in a collaborative project between the Oden Institute, MD Anderson Cancer Center, and TACC, which uses image-guided, reaction-diffusion equations to predict the response of triple negative breast cancer to neoadjuvant therapy on a patient-specific basis. In addition, she is working as one of the primary investigators in a collaboration with investigators from UT San Antonio to develop optimization scheme of convection-enhanced delivery of radioactive nanoliposomes for individual patients with recurrent glioblastoma. “The collaboration between medical imaging and computation has been appreciated increasingly. Tons of investigations have proved that the new computational tools can help extract clinically valuable information from imaging data which are originally hard to be well interpreted, while the advances of new imaging techniques make it possible to apply computational techniques for calibration, prediction and personalization,” describes Dr. Wu, “I believe the mathematics and computational sciences to be the real game changer of modern medicine, and I am devoted to make contribution to turning the potential into the reality.”

Publications:
1. Wu C, Hormuth DA, Pineda F, Karczmar GS, Moser RD, Yankeelov TE. Characterization of patient-specific drug delivery for breast cancer using image-guided computational fluid dynamics. Cancer Research. 2020;80(16 Supplement):4263. https://doi.org/10.1158/1538-7445.AM2020-4263.
2. Wu C, Hormuth DA, Oliver TA, Pineda F, Karczmar GS, Lorenzo G, Moser RD, Yankeelov TE. Patient-specific characterization of breast cancer hemodynamics using image-guided computational fluid dynamics. IEEE-TMI. 2020;39(9):2760-2771. https://doi.org/10.1109/TMI.2020.2975375.
3. 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. Magnetic resonance in medicine. 2019;81(3):2147-2160. https://doi.org/10.1002/mrm.27529. 
4. 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 2021.
5. Hormuth DA, Jarrett AM, Lorenzo G, Lima EABF, Wu C, Chung C, Patt D, Yankeelov TE. Math, magnets, and medicine: enabling personalized oncology. Expert Review of Precision Medicine and Drug Development. https://doi.org/10.1080/23808993.2021.1878023.
6. 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. European Journal of Radiology. 2021;136:109534. https://doi.org/10.1016/j.ejrad.2021.109534.
7. Jarrett AM, Hormuth II 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. 2020;22(12):820-30. https://doi.org/10.1016/j.neo.2020.10.011. 
8. Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG. 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. 2020;6(2):203. https://doi.org/10.18383/j.tom.2020.00012. 
9. Virostko J, Higgins EM, Wu C, Sorace AG, Patt D, Goodgame B, Yankeelov TE. Abstract PD9-07: The Rate of Parenchymal Enhancement During DCE-MRI Reflects Response to Neoadjuvant Therapy. Cancer Research. 2020;80(4 Supplement):PD9-07. https://doi.org/10.1158/1538-7445.SABCS19-PD9-07. 
10. 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. 2019;116(3):323a-324a. https://doi.org/10.1016/j.bpj.2018.11.1754. 
11. Woodall RT, Hormuth DA, Abdelmalik MRA, Wu C, Feng X, Phillips WT, Bao A, Hughes TJR, Brenner AJ, Yankeelov TE. Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment.  Medical Imaging 2019: Physics of Medical Imaging. International Society for Optics and Photonics. 2019;10948:109483M. https://doi.org/10.1117/12.2512867. 
12. Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE. Repeatability, reproducibility, and accuracy of quantitative MRI of the breast in the community radiology setting. Journal of Magnetic Resonance Imaging. 2018;48(3):695-707. https://doi.org/10.1002/jmri.26011. 
13. Virostko J, Sorace AG, Wu C, Ekrut DA, 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. 2019;5(1):44. https://doi.org/10.18383/j.tom.2018.00019. 
14. 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 multi-site rCBV consistency from DSC-MRI imaging protocols and post-processing software across the NCI Quantitative Imaging Network sites using a Digital Reference Object (DRO). Tomography. 2019;5(1):110. https://doi.org/10.18383/j.tom.2018.00041.