  Prostate cancer is one of the most common cancers in men. It is characterized
by a slow growth and it can be diagnosed in an early stage by observing the
Prostate Specific Antigen (PSA). However, a relapse after the primary therapy
could arise and different growth characteristics of the new tumor are observed.
In order to get a better understanding of the phenomenon, a mathematical model
involving several parameters is considered. To estimate the values of the
parameters identifying the disease risk level a novel approach, based on
combining Particle Swarm Optimization (PSO) with a meshfree interpolation
method, is proposed.
