UoP maths seminar (2013-14)
Prostate cancer hormone treatment outcome prediction based on a clinical data validated mathematical model.
Speaker: Yang Kuang (Arizona State University)
Venue: Wednesday 22nd January 2014, 2pm, LG2.04a (refreshments provided)
Abstract: Cycling prostate cancer patients on and off intermittent androgen suppression (IAS) treatment may alleviate most serious side effects and postpones the development of the inevitable treatment resistance for many patients. Combining well formulated mathematical models with clinical data, we show that such treatment outcomes can be accurately predicted for majority of the seven patients with excellent clinical data. Earlier models assume that an androgen-independent cell population grow at a constant rate. Our more accurate model is adapted from proven ecological models that are based on resource ratio theory which in a nutshell states that the most limiting resource most strongly affect growth, death and adaptation processes. With patient clinical data validated model, clinicians may identify patients that are appropriate for the treatment and predict the maximum length of treatment for a given patient before they become resistant to the treatment, leading to more effective and friendly therapy.