In-silico approaches to prostate cancer

Prostate cancer is the most frequent male cancer. After radiotherapy treatment, the prostate specific antigen (PSA) is used as a biomarker indicating tumor reccurrence. However, it is not still fully clear how to use PSA data to inform clinical decisions, a field in which mathematical models can be helpful. Also, what are the best therapeutic schedules after recurrences combining available drugs, and what are the optimal use of novel theranostic tools are not clear. Mathematical models may help answering those questions.

Publications

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

G. Lorenzo, N. di Muzio, C. L. Deantoni, C. Cozzarini, A. Fodor, A. Briganti, F. Montorsi, V. M. Pérez-García, H. Gómez, A. Reali

iScience 25, 105430 (2022)
Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome

G. Lorenzo, V.M. Pérez-García, Alfonso Mariño, L.A. Pérez Romasanta, Alessandro Reali, H. Gómez

Journal of the Royal Society Interface 20190195 (2019)

Collaborators

Work in this field is done in collaboration with applied mathematicians, Guillermo Lorenzo (Oden Institute for Computational Oncology, Texas, USA), Héctor Gómez (Purdue University, USA) and Alessandro Reali (University of Pisa, Italy) and also with radiation oncologists: Dr. Luis Pérez Romasanta (Hospital U. De Salamanca, Spain), Dr. Nadia di Muzio, and other clinicians at San Raffaele Hospital (Italy), between others. The optimal use of theranostic tools in prostate cancer is developed with Dr Babak Saboury (NIH), Bethesda, USA.

In-silico approaches to prostate cancer

Publications

Collaborators

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