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

An RBF-PSO Based Approach for Modeling Prostate Cancer