Hi ! Welcome to my personal webpage


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


A.S. Kazerouni, A.N. Dula, A.M. Jarrett, G. Lorenzo, J.A. Weis, J.A. Bankson, E.Y. Chekmenev, F. Pineda, G.S. Karczmar, T.E. Yankeelov


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


Oncology and mechanics: landmark studies and promising clinical applications


S. Urcun, G. Lorenzo, D. Baroli, P.Y. Rohan, G. Sciumè, W. Skalli, V. Lubrano, S.P.A. Bordas


In: S.P.A. Bordas, D.S. Balint, Advances in Applied Mechanics, in press


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


C. Wu, G. Lorenzo, D.A. Hormuth II, E.A.B.F. Lima, K.P. Slavkova, J.C. DiCarlo, J. Virotsko, C.M. Phillips, D. Patt, C. Chung, T.E. Yankeelov


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


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


E.Y. Yang, G.R. Howard, A. Brock, T.E. Yankeelov, G. Lorenzo


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


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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Pages


Academic biography


A brief summary of my education and research experience


Achievements


Main honors, awards, and competitive grants that I have received during my career


Teaching


List of my experience in teaching university courses and supervising students


Contact


Guillermo Lorenzo, PhD


Marie Skłodowska-Curie global fellow


[email protected]


Oden Institute


The University of Texas at Austin


201 E. 24th Street, POB 2.128
1 University Station (C0200)
Austin, Texas 78712-1229


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