MRI-based Digital Twins for Predicting the Response of Breast Cancer to Therapy

University of Texas Our lab is focused on developing tumor forecasting methods by integrating advanced imaging technologies with mathematical models to predict tumor growth and treatment response. In this presentation, we will discuss how quantitative magnetic resonance imaging data (MRI) can initialize and constrain mathematical models built on first-order effects related to proliferation, migration/invasion, vascular status, and drug-related treatment effects. More specifically, we will present some of our recent results through four vignettes focusing on breast cancer: 1) incorporating patient-specific data into mechanism-based mathematical models, 2) simulating outcomes via patient-specific digital twins, 3) rigorously guiding interventions through optimal control theory, and 4) updating interventions through data assimilation. The long-term goal is to provide a practical methodology that allows for optimizing therapeutic interventions on a patient-specific basis. For more information visit: https://lnkd.in/eSXV65VH *Contents* 00:00 - Introduction 04:04 - Presentation: Thomas Yankeelov: MRI-based Digital Twins for Predicting the Response of Breast Cancer to Therapy 48:08 - Questions Moderator: Tom Helikar, PhD, University of Nebraska, Lincoln To view the slides in this video visit: https://drive.google.com/file/d/1KuFkwEZTnVF4EFBGZHuX3_9JTqQ1aDJn/view?usp=sharing If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/