Personalized prediction of PSA dynamics after external radiotherapy of prostate cancer

 External beam radiation therapy is a widespread treatment for prostate cancer. The ensuing patient follow-up is based on the evolution of the prostate-specific antigen (PSA), which is an ubiquitous biomarker of pristate cancer measured in a blood test. Serum levels of PSA decay due to the radiation-induced death of tumor cells and cancer recurrence usually manifests as a rising PSA. 
The current definitions of biochemical relapse require that PSA reaches a minimum value (nadir) and starts increasing, which delays the use of further treatments. Also, these criteria for relapse do not account for the post-radiation tumor dynamics, which may contain early information to identify cancer recurrence. 
To address these issues, this project aims at developing new biomarkers for the early detection of PSA relapse after external beam radiation therapy based on mechanistic mathematical models of tumor and PSA dynamics. 
Figure. Our mathematical models assume that serum PSA is proportional to the number of cancer cells in the patient's tumor. After each radiation dose, the cancerous cells either survive and continue proliferating, or are irreversibly damaged and ultimately die (left). This figure shows an example of serum PSA modeling results for a patient exhibiting a relapse (center) and a cured patient (right), respectively, including patient-specific model forecasts and 95% confidence intervals. 


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

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

Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome

Guillermo Lorenzo, Víctor M. Pérez-García, Alfonso Mariño, Luis A. Pérez-Romasanta, Alessandro Reali, Hector Gomez

Journal of the Royal Society Interface, vol. 16(157), 2019, p. 20190195