Using clinically calibrated digital twins to generate personalized decision support for evolutionary therapy

Add to your list(s) Download to your calendar using vCal

• Mark Robertson-Tessi (Moffitt Cancer Center)
• Friday 19 September 2025, 10:45-10:50
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact nobody.

OOEW07 - Mathematical Foundations of Oncological Digital Twins

We developed a digital twin framework for guiding therapy decisions for individual patients. Given that cancer is an eco-evolutionary system that adapts to evade therapy, static treatment regimens eventually fail in many advanced cancers. A personalized, proactive, and dynamic approach to therapy has the potential to improve outcomes. To explore clinical feasibility of implementing such a framework, we developed a clinical trial (NCT04343365), the Evolutionary Tumor Board (ETB), which uses eco-evolutionary theory and predictive modeling to assist clinical decision making for individual cancer patients (n=24, ongoing). The framework relies on detailed data curation and imaging measurements for each patient, and mathematical modeling incorporates multi-lesion tumor growth, treatment response, and evolution of resistance. Digital twin models are calibrated by historical datasets of similar patients, clinical trial data, and the patient’s longitudinal data. A “Phase i trial” approach accounts for prediction uncertainty, and information is delivered via a software interface. The ETB has provided novel and useful decision support for the patients and oncologists. At the same time, our efforts show that there are both challenges and opportunities in using digital twins to predict and personalize therapy, particularly in the context of real-time clinical care.  

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.