Guillermo Lorenzo


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


Prostate cancer is a major health problem among ageing men worldwide. The current clinical management of this pathology enables its detection at early organ-confined stages by combining regular screening and patient classification in risk groups. 

However, the limited individualization of the clinical management beyond risk-group definition has led to significant overtreatment and undertreatment rates, which might adversely impact the patients’ lives and life expectancy, respectively. Thus, prostate cancer is a paradigmatic disease in which an individualized predictive technology could make a crucial difference in clinical practice, thereby separating less aggressive tumors that could be safely monitored from lethal tumors that require immediate treatment. 

To address this critical need, I leverage routine patient-specific clinical and imaging data to construct and parameterize personalized mathematical models of prostate cancer growth, with which I can perform computational forecasts of the patient's tumor prognosis to improve diagnosis and clinical decision-making on a patient-specific basis. 
Figure. Organ-scale, patient-specific prediction of prostate cancer growth. First, we obtain the imaging and clinical data from the patient. Then, a 3D geometric model of the prostate is created from the organ segmentation in anatomic magnetic resonance images. The tumor segmentation is projected over this geometric model. Finally, we run a personalized simulation of prostate cancer growth, estimating the parameters in the model equations from the patient’s longitudinal clinical and imaging data.

Publications


Quantitative in vivo imaging to enable tumor forecasting and treatment optimization


Guillermo Lorenzo, David A. Hormuth, Angela M. Jarrett, Ernesto A. B. F. Lima, Shashank Subramanian, George Biros, J. Tinsley Oden, Thomas J. R. Hughes, Thomas E. Yankeelov


arXiv: Tissues and Organs, 2021


Math, magnets, and medicine: enabling personalized oncology


David A. Hormuth, Angela M. Jarrett, Guillermo Lorenzo, Ernesto A. B. F. Lima, Chengyue Wu, Caroline Chung, Debra Patt, Thomas E. Yankeelov


Expert Review of Precision Medicine and Drug Development, vol. 6(2), 2021, pp. 79-81


A numerical simulation study of the dual role of 5α-reductase inhibitors on tumor growth in prostates enlarged by benign prostatic hyperplasia via stress relaxation and apoptosis upregulation


Guillermo Lorenzo, Thomas J.R. Hughes, Alessandro Reali, Hector Gomez


Computer Methods in Applied Mechanics and Engineering, 2020, p. 112843


Computer simulations suggest that prostate enlargement due to benign prostatic hyperplasia mechanically impedes prostate cancer growth


Guillermo Lorenzo, Thomas J. R. Hughes, Pablo Dominguez-Frojan, Alessandro Reali, Hector Gomez


Proceedings of the National Academy of Sciences of the United States of America, vol. 116(4), 2019, pp. 1152-1161


Hierarchically refined and coarsened splines for moving interface problems, with particular application to phase-field models of prostate tumor growth


Guillermo Lorenzo, Michael A. Scott, Kevin Tew, Thomas J.R. Hughes, Hector Gomez


Computer Methods in Applied Mechanics and Engineering, 2017, pp. 515-548


Tissue-scale, personalized modeling and simulation of prostate cancer growth


Guillermo Lorenzo, Michael A. Scott, Kevin Tew, Thomas J. R. Hughes, Yongjie Jessica Zhang, Lei Liu, Guillermo Vilanova, Hector Gomez


Proceedings of the National Academy of Sciences of the United States of America, vol. 113(48), 2016, pp. E7663-E7671