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Heiko Enderling

Heiko Enderling

Regular Member


University of Texas MD Anderson Cancer Center at Houston
Department of Radiation Oncology

My research is focused on developing and applying data science approaches and mechanistic mathematical modeling techniques to decipher tumor growth and treatment response dynamics to individualize cancer therapy. I develop clinically motivated quantitative models that are informable with patient-specific data for personalized treatment recommendations – Quantitative Personalized Oncology. This positions us at the forefront of the advent of digital twins and virtual trials that help provide optimal, individualized cancer treatment. I have pioneered the concept of training mathematical models to clinical outcomes instead of focusing on data fitting, which has substantially increased the predictive power and clinical utility of mechanistic modeling. Our lab developed the first-of-its-kind mechanistic mathematical model of tumor growth and radiotherapy (RT) response that can be trained with routinely collected radiological images prior to and during therapy, and can identify individual patient candidates for hyper- or hypofractionation RT. This work has led to the first prospective clinical trial of a mathematically informed biomarker for RT fractionation personalization in head and neck cancers, which led to significantly improved volumetric responses.

A student working in this environment will become familiar with mathematical and computational modeling techniques and how to apply these to radiation therapy. Relevant possible projects, techniques and learner activities in our research group may include:

-  Mathematical models of tumor growth and treatment response dynamics (ordinary and partial differential equations).

-  Computational agent-based models of tumor growth and treatment response dynamics.

-  Spatial statistics to decipher novel biomarkers in patient tissues and radiology images that correlate with treatment response and outcomes.

-  Machine learning and AI approaches to generate in silico data for model development, training, and testing

For all projects trainees would participate in project design, model development, numerical implementation and simulation.


MDACC Faculty


Education & Training

PhD, University of Dundee, 2006