Dosimetric evaluation of the capabilities of two clinical treatment planning systems for prostate cancer

Treatment planning systems play a key role in radiotherapy. Various commercial planning systems are currently available on the market. These systems usually differ in the algorithms used for radiation dose calculation and vary in the manner of implementation of the same algorithms. They also differ in optimizer algorithms used in search of the best treatment plan required to meet the user specified criteria. In this study, we evaluate two optimization systems available from two commercial treatment planning systems. The two systems used in the study are the Eclipse Planning System (version 10.042, Varian Medical Systems, Palo Alto); and, Monaco Planning System (version 3.01, Elekta CMS Software, St. Louis, MO). Computed tomographic images for ten patients, previously planned using Eclipse, were randomly selected from our patient database. We generated treatment plans for the ten cases utilizing the Monaco system. Monaco utilizes a set of biological and DVH functions in the optimization process, while Eclipse uses only dose volume objectives. Planners would need to understand the difference between the two methodologies for getting better outcomes in radiation treatment planning for cancer patients. All generated plans in the study were evaluated based on dose volume histogram (DVH) and isodose distributions. The overall performance of dose volume optimization was better compared to the biological optimization. Dose volume optimization was more efficient and easier to manipulate for our prostate cases. The biological optimization process provided good quality plans. However, the optimizer requires improvement to become more efficient. •Two optimization systems available from two commercial treatment planning systems were evaluated.•The overall performance of dose volume optimization was better compared to the biological optimization.•Dose volume optimization was more efficient and easier to manipulate for our prostate cases.•The biological optimization process was also able to provide good quality plans.•Biological optimizers require improvement to become more efficient.