Patient-specific, imaging-based forecasting of prostate cancer growth
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.
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
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
Guillermo Lorenzo, Thomas J.R. Hughes, Alessandro Reali, Hector Gomez
Computer Methods in Applied Mechanics and Engineering, 2020, p. 112843
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
Guillermo Lorenzo, Michael A. Scott, Kevin Tew, Thomas J.R. Hughes, Hector Gomez
Computer Methods in Applied Mechanics and Engineering, 2017, pp. 515-548
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