Research
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A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin
Abstract
ABSTRACT Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tu...
Publication · November 08, 2025
Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology
Abstract
Quantifying the uncertainty in predictive models is critical for establishing trust and enabling risk-informed decision making for personalized medicine. In contrast to one-size-fits-all approaches that seek to mitigate risk at the popu...
Publication · November 08, 2025
A critical assessment of artificial intelligence in magnetic resonance imaging of cancer
Abstract
Given the enormous output and pace of development of artificial intelligence (AI) methods in medical imaging, it can be challenging to identify the true success stories to determine the state-of-the-art of the field. This report seeks t...
Publication · November 08, 2025
Validating the predictions of mathematical models describing tumor growth and treatment response
Abstract
Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cance...
Publication · November 08, 2025
Forecasting chemoradiation response mid-treatment for high-grade gliomas through patient-specific biology-based modeling.
Publication · November 08, 2025
Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin
Abstract
Despite advances triple negative breast cancer treatment, ~50% of patients will not achieve a pathological complete response prior to surgery with standard of care neoadjuvant therapy (NAT). We hypothesize that personalized regimens for...
Publication · November 08, 2025
Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth
Abstract
Simple Summary Understanding how brain tumors grow over time is important for improving and personalizing treatment. In this study, we used mathematical models to simulate tumor growth in the brain and tested how well these models can p...
Publication · November 08, 2025
Speaking mathematical models into existence.
Abstract
Mathematical and computational modeling enables in silico testing of (for example) hypotheses, experimental design, and interventional strategies. However, building, sharing, and applying complex models require technical skills and soft...
Publication · November 08, 2025
A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation
Abstract
High-grade gliomas are highly invasive and respond variably to chemoradiation. Accurate, patient-specific predictions of tumor response could enhance treatment planning. We present a novel computational platform that assimilates MRI dat...
Publication · November 08, 2025
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