Image-Informed Mathematical Modeling to Predict Patient-Specific Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer
Event Type
Workshop
Applications
Computational Science
Education and Training and Outreach
HPC Community Collaboration
HPC Training and Education
Machine Learning and Artificial Intelligence
Performance
Workforce
W
TimeSunday, 14 November 20214:15pm - 4:30pm CST
Location223
DescriptionPatients 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. Preliminary results demonstrate the potential of the clinical-computational framework as a powerful tool for predicting response to NAT. Ongoing efforts include applying the approach to the whole patient cohort and performing the systematic model selection. Once validated, the approach could also assist in optimizing treatment plans on a patient-specific basis or guiding patient selection in trials for novel NAT regimens.
Author/Presenter
