Guillermo Lorenzo

I am a researcher in computational oncology.

The focus of my investigations is to build personalized mathematical models of the biophysical mechanisms underlying cancer growth and therapeutic response, with which I can make patient-specific forecasts of tumor prognosis using computer simulations. I believe that this predictive approach can dramatically contribute to advance clinical practice in oncology by delivering personalized solutions aiming at optimizing clinical outcomes for each patient.

I also work in modeling the mechanisms of development, treatment, and spread of other pathologies. Additionally, I am interested in constructing robust computational methods to efficiently and accurately solve my models in clinically-relevant times.

Thanks to a Marie Skłodowska-Curie global fellowship, I am currently working at the Center for Computational Oncology at the Oden Institute in The University of Texas at Austin, but I am also a postdoctoral fellow at Computational Mechanics & Advanced Materials Group at the University of Pavia in Italy.

Check out my research below and feel free to contact me if you are interested in discussing my work or any of the topics in this webpage. I am always open to new collaborations, delivering academic and popular science talks, or having a drink to talk science and else.

Research

Patient-specific, imaging-based forecasting of prostate cancer growth

This research aims at integrating standard clinical and imaging data from individual patients into mathematical models to enable the prediction of tumor growth using computer simulations

Personalized prediction of PSA dynamics after external radiotherapy of prostate cancer

Exploring the biophysical mechanisms underlying PSA dynamics after external radiotherapy to define new biomarkers for the early identification of relapse

Optimal control of therapeutic regimens for advanced prostate cancer

This work aims at finding optimal combinations of cytotoxic and antiangiogenic therapies to treat advanced prostatic tumors by combining mathematical analysis and computer simulations

Integrating multiscale data and mechanistic models to predict breast cancer response to neoadjuvant therapies

Personalized prediction of breast cancer response to neoadjuvant therapies by using biophysical models parameterized with patient-specific imaging data and constrained by comprehensive pharmacodynamic experimental data

Data-driven mechanistic models to forecast COVID-19 outbreaks

Constructing mathematical models to understand and predict the dynamics of COVID-19 infectious spread based on longitudinal epidemiological data series

Publications

Chapter 25. Emerging Techniques in breast MRI

Anum S. Kazerouni, Adrienne N. Dula, Angela M. Jarrett, Guillermo Lorenzo, Jared A. Weis, James A. Bankson, Eduard Y. Chekmenev, Federico Pineda, Gregory S. Karczmar, Thomas E. Yankeelov

In: K. Pinker, R. Mann, S. Partridge, Breast MRI: State of the Art and Future Directions, Elsevier, pp. 503-531

Integrating mechanism-based modeling with biomedical imaging to build digital twins for clinical oncology

Chengyue Wu, Guillermo Lorenzo, David A. Hormuth II, Ernesto A.B.F. Lima, Kalina P. Slavkova, Julie C. DiCarlo, John Virotsko, Caleb M. Phillips, Debra Patt, Caroline Chung, Thomas E. Yankeelov

Biophysics Review, vol. 3(2), 2022, p. 021304

Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin

Emily Y. Yang, Grant R. Howard, Amy Brock, Thomas E. Yankeelov, Guillermo Lorenzo

Frontiers in Molecular Biosciences, vol. 9, 2022, p. 972146

Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse

Guillermo Lorenzo, Nadia di Muzio, Chiara Lucrezia Deantoni, Cesare Cozzarini, Andrei Fodor, Alberto Briganti, Francesco Montorsi, Víctor M. Pérez-García, Hector Gomez, Alessandro Reali

iScience, vol. 25(11), 2022, p. 105430

Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

Guillermo Lorenzo, David A Hormuth II, Angela M Jarrett, Ernesto ABF Lima, Shashank Subramanian, George Biros, J Tinsley Oden, Thomas JR Hughes, Thomas E Yankeelov

In: Igor Balaz, Andrew Adamatzky, Cancer, Complexity, Computation, Springer, 2022, pp. 55-97

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