Advantage of biological over physical optimization in prostate cancer?

The biological effects of an applied dose can be accounted for by using biological objective functions with IMRT. A commonly used concept is the generalized equivalent uniform dose (gEUD), developed by Niemierko. Unlike the equivalent uniform dose (EUD) which is defined for tumor only, the gEUD can be used for both target volume and organs-at-risk (OAR). In this study, the gEUD has been integrated in our in-house inverse treatment planning system DMCO. DMCO is based on an inverse kernel concept and maintains full Monte-Carlo precision. The system applies direct aperture optimization by means of simulated annealing. Thereby DMCO is per se predestined for the optimization of non-quadratic biological objective functions. In this work, the feasibility of gEUD-based optimization with DMCO is investigated and compared to modified physical optimization. A 'pseudo' Pareto study is performed in order to derive the gEUD-parameters 'a' for the volumes-of-interest of a prostate case. The best biological plan is compared to a physically optimized plan, based on dose-volume objectives (DVO). Furthermore, a hybrid objective function (OF) was developed. It consists of both a biological OF for the OARs and a physical OF for the PTV. The plans are compared to another physically optimized plan, which includes additional zero-DVOs in order to further improve OAR-sparing. As a result of the comparisons it turns out, that the biological OF may improve plan quality with regard to the OARs, but at the price of a degradation of the PTV. This disadvantage can be overcome by a hybrid OF, by which the advantages of both biological and physical OF can be combined. With the application of the physical OF with properly set zero-DVOs, a similar or even superior plan quality may be achieved. The physical OFs do not need the time consuming stochastic optimization, which is mandatory in biological optimization and which is included in DMCO. Furthermore, biological evaluation leaves plan quality rather similar compared to physical optimization, but it cares automatically for the target and the OARs.