University of Texas at Austin

Upcoming Event: Oden Institute Seminar

Image-informed Mathematical Modeling to Predict Patient-Specific Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer

Chengyue Wu, Postdoctoral Fellow, Center for Computational Oncology, Oden Institute, UT Austin

3:30 – 5PM
Tuesday Nov 2, 2021

POB 6.304

Abstract

Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and for improved surgical outcomes. A critical, unmet need is a method to accurately predict an individual patient’s response to NAT, thereby allowing for the opportunity to guide further interventions. In this work, we construct and apply a clinical-computational framework that integrates quantitative magnetic resonance imaging with physics-based, mathematical modeling to predict the response of TNBC early in the course of NAT. Specifically, multiparametric MRI was acquired in TNBC patients before, during, and after a standard NAT regimen as part of the MD Anderson Cancer Center TNBC Moonshot Program. An image processing pipeline was developed for pre-processing registrations, segmentation and meshing of breast tissues, and calculation and mapping of tissue parameters and quantities-of-interest. A predictive model was developed based on a reaction-diffusion equation, which accounts for tumor cell proliferation, mechanical-coupled tumor cell mobility, and delivery and decay of the administered therapies that induce tumor cell death. We initialize and calibrate the model using processed patient MRI collected early in the course of NAT, then predict and validate patient-specific dynamics of tumor development up to the end of NAT procedure. Preliminary results demonstrate the potential of this clinical-computational framework as a powerful tool for predicting response to NAT. The approach also has the potential to assist in optimizing treatment plans on a patient specific basis or guiding patient selection in trials for novel NAT regimens

Biography

Chengyue Wu received her B.S. in Biosciences (Biophysics and Neurobiology) from University of Science and Technology of China in 2016, and Ph.D. in Biomedical Engineering from The University of Texas at Austin in 2020. Dr. Wu is currently a Postdoctoral Research Fellow at the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin. Her research interests span the interdisciplinary field of computational medicine and biomedical imaging. Her previous research experience focused on developing and validating computational-experimental approaches for advancing magnetic resonance imaging techniques to improve the diagnostic specificity of breast cancer. Currently at the Oden Institute, her research is continuously focused on the imaging-based mathematical oncology. She serves on the Mathematical Oncology Subgroup Executive Committee of the Society for Mathematical Biology (SMB). She has also been regularly serving as a co-organizer, moderator, and/or reviewer on multiple scientific conferences, as well as a reviewer for several scientific journals.

Image-informed Mathematical Modeling to Predict Patient-Specific Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer

Event information

Date
3:30 – 5PM
Tuesday Nov 2, 2021
Location POB 6.304
Hosted by Tom Yankeelov