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There is a growing interest in the combined use of Stereotactic Body Radiation Therapy (SBRT) with Flattening Filter Free (FFF) due to the high local control rates and reduced treatment times, compared to conventionally fractionated treatments. It has been suggested that they may also provide a better radiation protection to radiotherapy patients as a consequence of the expected decrease in peripheral doses. This work aims to determine this reduction in unattended out-of-field regions, where no CT information is available but an important percentage of second primary cancers occur. For that purpose, ten different cases suitable for SBRT were chosen. Thus, 142 different treatment plans including SBRT, as well as 3D‐CRT, IMRT and VMAT (with standard fractionation) in low and high energies for Varian (FF and FFF), Siemens and Elekta machines were created. Then, photon and neutron peripheral dose in 14 organs were assessed and compared using two analytical models. For the prostate case, uncomplicated and cancer free control probability estimation was also carried out. As a general behavior, SBRT plans led to the lowest peripheral doses followed by 3D-CRT, VMAT and IMRT, in this order. Unflattened beams proved to be the most effective in reducing peripheral doses, especially for 10 MV. The obtained results suggest that FFF beams for SBRT with 10 MV represent the best compromise between dose delivery efficiency and peripheral dose reduction.

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Purpose To propose adaptive setup protocols using Bayesian statistics that facilitate, based on a prediction of coverage probability, making a decision on which patients should follow daily imaging prior to treatment delivery. Materials and Methods The suitability of the treatment margins was assessed combining interfraction variability measurements of the first days of treatment with previous data gathered from our patient population. From this information, we decided if a patient needs an online imaging protocol to perform daily isocenter correction before each treatment fraction. We applied our method to five different datasets. Protocol parameters were selected from each dataset based on coverage probability, the expected imaging workload of the treatment unit, and the accuracy of patient classification. Time trends were assessed and included in the proposed protocols. To validate the accuracy of the protocols, they were applied to a validation dataset of prostate cancer patients. Results Adaptive setup protocols lead expected population coverage >97% in all datasets analyzed when time trends were considered. The reduction in imaging workload ranged from 40% in lung treatments to 28.5% in prostate treatments. Results of the protocol on the validation dataset were very similar to those previously predicted. Conclusions The adaptive setup protocols based on Bayesian statistics presented in this study enable the optimization of imaging workload in the treatment unit ensuring that appropriate dose coverage remains unchanged.

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